UMKM_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2nP6hvt6XRAL"
   },
   "source": [
    "# Project Analisa Klasifikasi Pinjaman untuk Sektor UMKM\n",
    "<b>Describe</b><br>\n",
    "Perusahaan Alembert merupakan perusahaan yang bergerak di bidang layanan pinjaman usaha bagi sektor UMKM. Karena adanya pandemik ini, perusahaan berusaha memberikan pelayanan berupa keringanan pinjaman bagi pelanggan yang disebut sebagai rekomendasi tindak lanjut. Pemberian rekomendasi tindak lanjut pada pelanggan ini didasari pada kriteria tertentu, dan perlu ditentukan faktor-faktor apa saja yang berpengaruh sehingga pelanggan mendapatkan treatment tertentu yang masuk dalam rekomendasi tindak lanjut program dari perusahaan.\n",
    "\n",
    "<b>Tujuan Project:</b> Klasifikasi nasabah yang akan dimasukkan pada rekomendasi tindak lanjut. Pada kelas target rekomendasi tindak lanjut ini sendiri terdiri dari beberapa kelas seperti restrukturisasi dan angsuran biasa.\n",
    "\n",
    "<b>Model:</b> Regresi multinomial.\n",
    "\n",
    "<b>Referensi model (Ref):</b> Kelas pada rekomendasi tindak lanjut yang memiliki banyak pelanggan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "38wctP5rdNpT"
   },
   "source": [
    "### Membaca data eksternal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "pOduZq8xciGs",
    "outputId": "23f7d9d3-a7fe-4a60-b849-3fae74b0ba74",
    "vscode": {
     "languageId": "r"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 17</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X</th><th scope=col>NAMA_NASABAH</th><th scope=col>NOMOR_KONTRAK</th><th scope=col>DOMISILI</th><th scope=col>KARAKTER</th><th scope=col>PROFESI</th><th scope=col>KONDISI_USAHA</th><th scope=col>KONDISI_JAMINAN</th><th scope=col>STATUS</th><th scope=col>PRODUK</th><th scope=col>PYD</th><th scope=col>TENOR</th><th scope=col>OSL</th><th scope=col>KEWAJIBAN</th><th scope=col>KOLEKTIBILITAS</th><th scope=col>COUNT_SURVEY</th><th scope=col>REKOMENDASI_TINDAK_LANJUT</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0</td><td>YOLI SEPINA NAINGGOLAN</td><td>0</td><td>MASIH TETAP    </td><td>KOOPERATIF      </td><td>IBU RUMAH TANGGA    </td><td>2</td><td>Baik </td><td>2</td><td>3</td><td> 30000000</td><td>24</td><td>28750000</td><td>4896841</td><td>DALAM PENGAWASAN KHUSUS</td><td>1</td><td>Angsuran Biasa </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>ERWIN NASUTION        </td><td>1</td><td>MASIH TETAP    </td><td>TIDAK KOOPERATIF</td><td>NELAYAN             </td><td>3</td><td>Rusak</td><td>8</td><td>3</td><td> 10000000</td><td>12</td><td> 2040693</td><td>      0</td><td>MACET                  </td><td>1</td><td>Penarikan      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2</td><td>HUSIN                 </td><td>2</td><td>MASIH TETAP    </td><td>TIDAK KOOPERATIF</td><td>LAINNYA             </td><td>3</td><td>Baik </td><td>8</td><td>3</td><td> 60000000</td><td>24</td><td>       0</td><td>      0</td><td>MACET                  </td><td>1</td><td>Penarikan      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>3</td><td>HARITSYAH             </td><td>3</td><td>PINDAH PERMANEN</td><td>KOOPERATIF      </td><td>PNS                 </td><td>1</td><td>Rusak</td><td>7</td><td>3</td><td> 90000000</td><td>18</td><td>       0</td><td>      0</td><td>MACET                  </td><td>1</td><td>Angsuran Biasa </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>4</td><td>HARIRI PANGGABEAN     </td><td>4</td><td>MASIH TETAP    </td><td>TIDAK KOOPERATIF</td><td>WIRAUSAHA / PEDAGANG</td><td>1</td><td>Baik </td><td>8</td><td>3</td><td>150000000</td><td>18</td><td>19844807</td><td>      0</td><td>MACET                  </td><td>1</td><td>Penarikan      </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>5</td><td>JHON PREDDY HUTABARAT </td><td>5</td><td>MASIH TETAP    </td><td>KOOPERATIF      </td><td>WIRAUSAHA / PEDAGANG</td><td>1</td><td>Baik </td><td>7</td><td>3</td><td> 40000000</td><td>36</td><td>27298726</td><td>2208516</td><td>DALAM PENGAWASAN KHUSUS</td><td>1</td><td>Restrukturisasi</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 17\n",
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       "  & X & NAMA\\_NASABAH & NOMOR\\_KONTRAK & DOMISILI & KARAKTER & PROFESI & KONDISI\\_USAHA & KONDISI\\_JAMINAN & STATUS & PRODUK & PYD & TENOR & OSL & KEWAJIBAN & KOLEKTIBILITAS & COUNT\\_SURVEY & REKOMENDASI\\_TINDAK\\_LANJUT\\\\\n",
       "  & <int> & <chr> & <int> & <chr> & <chr> & <chr> & <int> & <chr> & <int> & <int> & <int> & <int> & <int> & <int> & <chr> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 0 & YOLI SEPINA NAINGGOLAN & 0 & MASIH TETAP     & KOOPERATIF       & IBU RUMAH TANGGA     & 2 & Baik  & 2 & 3 &  30000000 & 24 & 28750000 & 4896841 & DALAM PENGAWASAN KHUSUS & 1 & Angsuran Biasa \\\\\n",
       "\t2 & 1 & ERWIN NASUTION         & 1 & MASIH TETAP     & TIDAK KOOPERATIF & NELAYAN              & 3 & Rusak & 8 & 3 &  10000000 & 12 &  2040693 &       0 & MACET                   & 1 & Penarikan      \\\\\n",
       "\t3 & 2 & HUSIN                  & 2 & MASIH TETAP     & TIDAK KOOPERATIF & LAINNYA              & 3 & Baik  & 8 & 3 &  60000000 & 24 &        0 &       0 & MACET                   & 1 & Penarikan      \\\\\n",
       "\t4 & 3 & HARITSYAH              & 3 & PINDAH PERMANEN & KOOPERATIF       & PNS                  & 1 & Rusak & 7 & 3 &  90000000 & 18 &        0 &       0 & MACET                   & 1 & Angsuran Biasa \\\\\n",
       "\t5 & 4 & HARIRI PANGGABEAN      & 4 & MASIH TETAP     & TIDAK KOOPERATIF & WIRAUSAHA / PEDAGANG & 1 & Baik  & 8 & 3 & 150000000 & 18 & 19844807 &       0 & MACET                   & 1 & Penarikan      \\\\\n",
       "\t6 & 5 & JHON PREDDY HUTABARAT  & 5 & MASIH TETAP     & KOOPERATIF       & WIRAUSAHA / PEDAGANG & 1 & Baik  & 7 & 3 &  40000000 & 36 & 27298726 & 2208516 & DALAM PENGAWASAN KHUSUS & 1 & Restrukturisasi\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 17\n",
       "\n",
       "| <!--/--> | X &lt;int&gt; | NAMA_NASABAH &lt;chr&gt; | NOMOR_KONTRAK &lt;int&gt; | DOMISILI &lt;chr&gt; | KARAKTER &lt;chr&gt; | PROFESI &lt;chr&gt; | KONDISI_USAHA &lt;int&gt; | KONDISI_JAMINAN &lt;chr&gt; | STATUS &lt;int&gt; | PRODUK &lt;int&gt; | PYD &lt;int&gt; | TENOR &lt;int&gt; | OSL &lt;int&gt; | KEWAJIBAN &lt;int&gt; | KOLEKTIBILITAS &lt;chr&gt; | COUNT_SURVEY &lt;int&gt; | REKOMENDASI_TINDAK_LANJUT &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 0 | YOLI SEPINA NAINGGOLAN | 0 | MASIH TETAP     | KOOPERATIF       | IBU RUMAH TANGGA     | 2 | Baik  | 2 | 3 |  30000000 | 24 | 28750000 | 4896841 | DALAM PENGAWASAN KHUSUS | 1 | Angsuran Biasa  |\n",
       "| 2 | 1 | ERWIN NASUTION         | 1 | MASIH TETAP     | TIDAK KOOPERATIF | NELAYAN              | 3 | Rusak | 8 | 3 |  10000000 | 12 |  2040693 |       0 | MACET                   | 1 | Penarikan       |\n",
       "| 3 | 2 | HUSIN                  | 2 | MASIH TETAP     | TIDAK KOOPERATIF | LAINNYA              | 3 | Baik  | 8 | 3 |  60000000 | 24 |        0 |       0 | MACET                   | 1 | Penarikan       |\n",
       "| 4 | 3 | HARITSYAH              | 3 | PINDAH PERMANEN | KOOPERATIF       | PNS                  | 1 | Rusak | 7 | 3 |  90000000 | 18 |        0 |       0 | MACET                   | 1 | Angsuran Biasa  |\n",
       "| 5 | 4 | HARIRI PANGGABEAN      | 4 | MASIH TETAP     | TIDAK KOOPERATIF | WIRAUSAHA / PEDAGANG | 1 | Baik  | 8 | 3 | 150000000 | 18 | 19844807 |       0 | MACET                   | 1 | Penarikan       |\n",
       "| 6 | 5 | JHON PREDDY HUTABARAT  | 5 | MASIH TETAP     | KOOPERATIF       | WIRAUSAHA / PEDAGANG | 1 | Baik  | 7 | 3 |  40000000 | 36 | 27298726 | 2208516 | DALAM PENGAWASAN KHUSUS | 1 | Restrukturisasi |\n",
       "\n"
      ],
      "text/plain": [
       "  X NAMA_NASABAH           NOMOR_KONTRAK DOMISILI        KARAKTER        \n",
       "1 0 YOLI SEPINA NAINGGOLAN 0             MASIH TETAP     KOOPERATIF      \n",
       "2 1 ERWIN NASUTION         1             MASIH TETAP     TIDAK KOOPERATIF\n",
       "3 2 HUSIN                  2             MASIH TETAP     TIDAK KOOPERATIF\n",
       "4 3 HARITSYAH              3             PINDAH PERMANEN KOOPERATIF      \n",
       "5 4 HARIRI PANGGABEAN      4             MASIH TETAP     TIDAK KOOPERATIF\n",
       "6 5 JHON PREDDY HUTABARAT  5             MASIH TETAP     KOOPERATIF      \n",
       "  PROFESI              KONDISI_USAHA KONDISI_JAMINAN STATUS PRODUK PYD      \n",
       "1 IBU RUMAH TANGGA     2             Baik            2      3       30000000\n",
       "2 NELAYAN              3             Rusak           8      3       10000000\n",
       "3 LAINNYA              3             Baik            8      3       60000000\n",
       "4 PNS                  1             Rusak           7      3       90000000\n",
       "5 WIRAUSAHA / PEDAGANG 1             Baik            8      3      150000000\n",
       "6 WIRAUSAHA / PEDAGANG 1             Baik            7      3       40000000\n",
       "  TENOR OSL      KEWAJIBAN KOLEKTIBILITAS          COUNT_SURVEY\n",
       "1 24    28750000 4896841   DALAM PENGAWASAN KHUSUS 1           \n",
       "2 12     2040693       0   MACET                   1           \n",
       "3 24           0       0   MACET                   1           \n",
       "4 18           0       0   MACET                   1           \n",
       "5 18    19844807       0   MACET                   1           \n",
       "6 36    27298726 2208516   DALAM PENGAWASAN KHUSUS 1           \n",
       "  REKOMENDASI_TINDAK_LANJUT\n",
       "1 Angsuran Biasa           \n",
       "2 Penarikan                \n",
       "3 Penarikan                \n",
       "4 Angsuran Biasa           \n",
       "5 Penarikan                \n",
       "6 Restrukturisasi          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get Dataset\n",
    "data <- read.csv(\"https://storage.googleapis.com/dqlab-dataset/project.csv\")\n",
    "\n",
    "# Display the top 6 rows\n",
    "head(data, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rTTDqJURdrcs",
    "outputId": "1a9dd76d-b5b5-4ea6-fc4c-c733af39be95",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1000 obs. of  17 variables:\n",
      " $ X                        : int  0 1 2 3 4 5 6 7 8 9 ...\n",
      " $ NAMA_NASABAH             : chr  \"YOLI SEPINA NAINGGOLAN\" \"ERWIN NASUTION\" \"HUSIN\" \"HARITSYAH\" ...\n",
      " $ NOMOR_KONTRAK            : int  0 1 2 3 4 5 6 7 8 9 ...\n",
      " $ DOMISILI                 : chr  \"MASIH TETAP\" \"MASIH TETAP\" \"MASIH TETAP\" \"PINDAH PERMANEN\" ...\n",
      " $ KARAKTER                 : chr  \"KOOPERATIF\" \"TIDAK KOOPERATIF\" \"TIDAK KOOPERATIF\" \"KOOPERATIF\" ...\n",
      " $ PROFESI                  : chr  \"IBU RUMAH TANGGA\" \"NELAYAN\" \"LAINNYA\" \"PNS\" ...\n",
      " $ KONDISI_USAHA            : int  2 3 3 1 1 1 3 2 2 3 ...\n",
      " $ KONDISI_JAMINAN          : chr  \"Baik\" \"Rusak\" \"Baik\" \"Rusak\" ...\n",
      " $ STATUS                   : int  2 8 8 7 8 7 7 7 7 3 ...\n",
      " $ PRODUK                   : int  3 3 3 3 3 3 3 3 3 3 ...\n",
      " $ PYD                      : int  30000000 10000000 60000000 90000000 150000000 40000000 60000000 7500000 45000000 50000000 ...\n",
      " $ TENOR                    : int  24 12 24 18 18 36 36 4 24 36 ...\n",
      " $ OSL                      : int  28750000 2040693 0 0 19844807 27298726 19999200 7500000 45000000 6944100 ...\n",
      " $ KEWAJIBAN                : int  4896841 0 0 0 0 2208516 6946592 0 0 7730984 ...\n",
      " $ KOLEKTIBILITAS           : chr  \"DALAM PENGAWASAN KHUSUS\" \"MACET\" \"MACET\" \"MACET\" ...\n",
      " $ COUNT_SURVEY             : int  1 1 1 1 1 1 2 2 1 1 ...\n",
      " $ REKOMENDASI_TINDAK_LANJUT: chr  \"Angsuran Biasa\" \"Penarikan\" \"Penarikan\" \"Angsuran Biasa\" ...\n"
     ]
    }
   ],
   "source": [
    "# displays the data type of each column\n",
    "str(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xINNPMd3XRAN"
   },
   "source": [
    "##### Statistik deskriptif data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 624
    },
    "id": "Na5QYKTfeX8T",
    "outputId": "265dc1f3-afdf-439a-a001-41f0fd2e3555",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       X         NAMA_NASABAH       NOMOR_KONTRAK     DOMISILI        \n",
       " Min.   :  0.0   Length:1000        Min.   :  0.0   Length:1000       \n",
       " 1st Qu.:249.8   Class :character   1st Qu.:249.8   Class :character  \n",
       " Median :499.5   Mode  :character   Median :499.5   Mode  :character  \n",
       " Mean   :499.5                      Mean   :499.5                     \n",
       " 3rd Qu.:749.2                      3rd Qu.:749.2                     \n",
       " Max.   :999.0                      Max.   :999.0                     \n",
       "   KARAKTER           PROFESI          KONDISI_USAHA   KONDISI_JAMINAN   \n",
       " Length:1000        Length:1000        Min.   :1.000   Length:1000       \n",
       " Class :character   Class :character   1st Qu.:2.000   Class :character  \n",
       " Mode  :character   Mode  :character   Median :2.000   Mode  :character  \n",
       "                                       Mean   :2.273                     \n",
       "                                       3rd Qu.:3.000                     \n",
       "                                       Max.   :3.000                     \n",
       "     STATUS          PRODUK           PYD                TENOR      \n",
       " Min.   :2.000   Min.   : 3.00   Min.   :   500000   Min.   : 3.00  \n",
       " 1st Qu.:3.000   1st Qu.: 3.00   1st Qu.:  8000000   1st Qu.:12.00  \n",
       " Median :7.000   Median : 3.00   Median : 15000000   Median :18.00  \n",
       " Mean   :5.379   Mean   :12.45   Mean   : 38537508   Mean   :20.75  \n",
       " 3rd Qu.:7.000   3rd Qu.: 7.00   3rd Qu.: 50000000   3rd Qu.:24.00  \n",
       " Max.   :8.000   Max.   :77.00   Max.   :500000000   Max.   :48.00  \n",
       "      OSL              KEWAJIBAN         KOLEKTIBILITAS      COUNT_SURVEY  \n",
       " Min.   :        0   Min.   :        0   Length:1000        Min.   :1.000  \n",
       " 1st Qu.:  3999950   1st Qu.:   687487   Class :character   1st Qu.:1.000  \n",
       " Median :  8687350   Median :  2008974   Mode  :character   Median :1.000  \n",
       " Mean   : 26562373   Mean   :  5663981                      Mean   :1.036  \n",
       " 3rd Qu.: 32082900   3rd Qu.:  4823198                      3rd Qu.:1.000  \n",
       " Max.   :440932336   Max.   :400900000                      Max.   :2.000  \n",
       " REKOMENDASI_TINDAK_LANJUT\n",
       " Length:1000              \n",
       " Class :character         \n",
       " Mode  :character         \n",
       "                          \n",
       "                          \n",
       "                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary\n",
    "summary(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nq7aeBpZXRAN"
   },
   "source": [
    "### Menghapus kolom\n",
    "Pada data, sebenarnya tidak memerlukan NAMA_NASABAH untuk diberikan rekomendasi. Atau dengan kata lain penanda pelanggan untuk diberikan rekomendasi cukup dengan melihat no_kontrak pelanggan itu saja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "amIK3z-femZO",
    "outputId": "4212b400-282a-4e98-8e3b-b3121e83d10a",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'NOMOR_KONTRAK'</li><li>'DOMISILI'</li><li>'KARAKTER'</li><li>'PROFESI'</li><li>'KONDISI_USAHA'</li><li>'KONDISI_JAMINAN'</li><li>'STATUS'</li><li>'PRODUK'</li><li>'PYD'</li><li>'TENOR'</li><li>'OSL'</li><li>'KEWAJIBAN'</li><li>'KOLEKTIBILITAS'</li><li>'COUNT_SURVEY'</li><li>'REKOMENDASI_TINDAK_LANJUT'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'NOMOR\\_KONTRAK'\n",
       "\\item 'DOMISILI'\n",
       "\\item 'KARAKTER'\n",
       "\\item 'PROFESI'\n",
       "\\item 'KONDISI\\_USAHA'\n",
       "\\item 'KONDISI\\_JAMINAN'\n",
       "\\item 'STATUS'\n",
       "\\item 'PRODUK'\n",
       "\\item 'PYD'\n",
       "\\item 'TENOR'\n",
       "\\item 'OSL'\n",
       "\\item 'KEWAJIBAN'\n",
       "\\item 'KOLEKTIBILITAS'\n",
       "\\item 'COUNT\\_SURVEY'\n",
       "\\item 'REKOMENDASI\\_TINDAK\\_LANJUT'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'NOMOR_KONTRAK'\n",
       "2. 'DOMISILI'\n",
       "3. 'KARAKTER'\n",
       "4. 'PROFESI'\n",
       "5. 'KONDISI_USAHA'\n",
       "6. 'KONDISI_JAMINAN'\n",
       "7. 'STATUS'\n",
       "8. 'PRODUK'\n",
       "9. 'PYD'\n",
       "10. 'TENOR'\n",
       "11. 'OSL'\n",
       "12. 'KEWAJIBAN'\n",
       "13. 'KOLEKTIBILITAS'\n",
       "14. 'COUNT_SURVEY'\n",
       "15. 'REKOMENDASI_TINDAK_LANJUT'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"NOMOR_KONTRAK\"             \"DOMISILI\"                 \n",
       " [3] \"KARAKTER\"                  \"PROFESI\"                  \n",
       " [5] \"KONDISI_USAHA\"             \"KONDISI_JAMINAN\"          \n",
       " [7] \"STATUS\"                    \"PRODUK\"                   \n",
       " [9] \"PYD\"                       \"TENOR\"                    \n",
       "[11] \"OSL\"                       \"KEWAJIBAN\"                \n",
       "[13] \"KOLEKTIBILITAS\"            \"COUNT_SURVEY\"             \n",
       "[15] \"REKOMENDASI_TINDAK_LANJUT\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Deleting columns\n",
    "data_reduce = data[-c(1,2)]\n",
    "colnames(data_reduce)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xgzTmE1tXRAO"
   },
   "source": [
    "### Konversi data\n",
    "Seperti yang diketahui ketika data ditarik dari suatu sumber terkadang ada kondisi tipe data tidak dengan tepat direpresentasikan. Misalkan semua record/baris pada suatu kolom berisi seharusnya data numerik akan tetapi disajikan didalam suatu karakter angka.\n",
    "\n",
    "R sendiri memiliki fungsi sapply yang dapat digunakan untuk mengkoversi tipe data. Dalam hal ini fungsi sapply menerima input/argumen fungsi berupa list, vector, atau data frame dan mengembalikan/menghasilkan output berupa vector atau matrix.\n",
    "\n",
    "Cobalah untuk meninjau kembali kolom PRODUK, PYD, TENOR, dan OSL apakah perlu dikonversikan menjadi bertipe numerik atau tidak. Jika tidak perlu di konversi, tidak perlu diubah."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "0_kYvYdAeSos",
    "outputId": "c1db728a-eb97-4d89-b71f-4f86f3980149"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>PRODUK</th><th scope=col>PYD</th><th scope=col>TENOR</th><th scope=col>OSL</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>3</td><td> 30000000</td><td>24</td><td>28750000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>3</td><td> 10000000</td><td>12</td><td> 2040693</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td> 60000000</td><td>24</td><td>       0</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>3</td><td> 90000000</td><td>18</td><td>       0</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>3</td><td>150000000</td><td>18</td><td>19844807</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>3</td><td> 40000000</td><td>36</td><td>27298726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & PRODUK & PYD & TENOR & OSL\\\\\n",
       "  & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 3 &  30000000 & 24 & 28750000\\\\\n",
       "\t2 & 3 &  10000000 & 12 &  2040693\\\\\n",
       "\t3 & 3 &  60000000 & 24 &        0\\\\\n",
       "\t4 & 3 &  90000000 & 18 &        0\\\\\n",
       "\t5 & 3 & 150000000 & 18 & 19844807\\\\\n",
       "\t6 & 3 &  40000000 & 36 & 27298726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | PRODUK &lt;int&gt; | PYD &lt;int&gt; | TENOR &lt;int&gt; | OSL &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 3 |  30000000 | 24 | 28750000 |\n",
       "| 2 | 3 |  10000000 | 12 |  2040693 |\n",
       "| 3 | 3 |  60000000 | 24 |        0 |\n",
       "| 4 | 3 |  90000000 | 18 |        0 |\n",
       "| 5 | 3 | 150000000 | 18 | 19844807 |\n",
       "| 6 | 3 |  40000000 | 36 | 27298726 |\n",
       "\n"
      ],
      "text/plain": [
       "  PRODUK PYD       TENOR OSL     \n",
       "1 3       30000000 24    28750000\n",
       "2 3       10000000 12     2040693\n",
       "3 3       60000000 24           0\n",
       "4 3       90000000 18           0\n",
       "5 3      150000000 18    19844807\n",
       "6 3       40000000 36    27298726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cek.1 <- data_reduce[, 8:11]\n",
    "head(cek.1, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vp9M2VczXRAO"
   },
   "source": [
    "### Pemilihan data kategori\n",
    "Tentu saja kamu menyadari bahwa data yang dimiliki ada yang bersifat kategorik. Data kategori dapat dipilih melalui kolom-kolom “KONDISI_USAHA”, “KONDISI_JAMINAN”, “REKOMENDASI_TINDAK_LANJUT”.\n",
    "\n",
    "Kemudian ubah kolom “REKOMENDASI_TINDAK_LANJUT” sebagai faktor (menggunakkan as.factor).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "UMAOAddvfWt4",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Select spesific columns from the DataFrame\n",
    "data_kategorik = data_reduce[, c(\"KONDISI_USAHA\", \"KONDISI_JAMINAN\", \"REKOMENDASI_TINDAK_LANJUT\")]\n",
    "\n",
    "# Change the type data of column \"REKOMENDASI_TINDAK_LANJUT\"\n",
    "data_reduce$REKOMENDASI_TINDAK_LANJUT = as.factor(data_reduce$REKOMENDASI_TINDAK_LANJUT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VhEcmTPxh08h",
    "outputId": "74193686-9378-4778-c683-4c3058481612"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Factor w/ 4 levels \"Angsuran Biasa\",..: 1 3 3 1 3 4 4 4 4 1 ...\n"
     ]
    }
   ],
   "source": [
    "str(data_reduce$REKOMENDASI_TINDAK_LANJUT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WKwLCKd2gUrA"
   },
   "source": [
    "\n",
    "Gunakan uji chi-square dapat digunakan untuk melihat hubungan antar variabel kategorik berikut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "id": "IzKQo-q8gTZM",
    "outputId": "ca638b44-9509-4f25-e8ca-92b796ac4936",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in chisq.test(data_kategorik$KONDISI_USAHA, data_kategorik$REKOMENDASI_TINDAK_LANJUT):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test\n",
       "\n",
       "data:  data_kategorik$KONDISI_USAHA and data_kategorik$REKOMENDASI_TINDAK_LANJUT\n",
       "X-squared = 129.82, df = 6, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View relationship between data variable\n",
    "chisq.test(data_kategorik$KONDISI_USAHA, data_kategorik$REKOMENDASI_TINDAK_LANJUT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "id": "-B065Xd3gott",
    "outputId": "0a760e70-4394-4365-82eb-761bb3bcfdca",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in chisq.test(data_kategorik$KONDISI_JAMINAN, data_kategorik$REKOMENDASI_TINDAK_LANJUT):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test\n",
       "\n",
       "data:  data_kategorik$KONDISI_JAMINAN and data_kategorik$REKOMENDASI_TINDAK_LANJUT\n",
       "X-squared = 162.87, df = 9, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View relationship between KONDISI_JAMINAN and REKOMENDASI_TINDAK_LANJUT columns\n",
    "chisq.test(data_kategorik$KONDISI_JAMINAN, data_kategorik$REKOMENDASI_TINDAK_LANJUT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7PkodEGEXRAO"
   },
   "source": [
    "Berdasarkan hasil temuan dari uji chi-square diatas dapat disimpulkan bahwa variabel kondisi usaha memiliki hubungan dengan variabel rekomendasi tindak lanjut karena p-value < 0,05(5%). Kondisi jaminan juga memiliki hubungan dengan variabel rekomendasi tindak lanjut karena p-value < 0,05(5%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P-zxuUa-XRAO"
   },
   "source": [
    "### Korelasi antar variabel data\n",
    "Selain melihat hubungan pada data yang bersifat kategorikal, kita juga bisa melihat hubungan antar variabel numerikal. Ya. Kita akan menggunakan korelasi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O1stNDARiGTY",
    "outputId": "9db4d168-bd70-4355-9e43-f6e4d4c2be11",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages('corrplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 489
    },
    "id": "ihDxqKF5hFWV",
    "outputId": "8fc2c124-689a-4923-e0b2-2546a9a6fc46",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "corrplot 0.92 loaded\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'PRODUK'</li><li>'PYD'</li><li>'TENOR'</li><li>'OSL'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'PRODUK'\n",
       "\\item 'PYD'\n",
       "\\item 'TENOR'\n",
       "\\item 'OSL'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'PRODUK'\n",
       "2. 'PYD'\n",
       "3. 'TENOR'\n",
       "4. 'OSL'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"PRODUK\" \"PYD\"    \"TENOR\"  \"OSL\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VWvL9e2d1WxBT9V5nbBWY84HQV+MF/EMz2itlwOtSdSSJLnQ6wo9xCSAGA2e/5r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\nkWaQISUUsLTJdRxjVw8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dGFe27WlrY+djNnIj8f48a5/xzkD5So65jYkdsuXUJA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32Tf71A6kknq2i4+NDnd6WKcTNxojirPt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\nOwAAAACdwGAHAAAAoBMY7AAAAAB04heqhEEey5jsQgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(corrplot)\n",
    "\n",
    "M = data_reduce[,8:11]\n",
    "colnames(M)\n",
    "\n",
    "# Pearson Correlation\n",
    "par(mfrow=c(2,2))\n",
    "corrplot(cor(M), type = \"upper\", order = \"hclust\")\n",
    "corrplot(cor(M), method = \"square\", type = \"upper\")\n",
    "corrplot(cor(M), method = \"number\", type = \"lower\")\n",
    "corrplot(cor(M), method = \"ellipse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I32GaZm2jKYU",
    "outputId": "a44195ff-99f4-49c1-9f0b-08840a8ffef0",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "also installing the dependencies ‘plyr’, ‘Rcpp’, ‘reshape2’\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages('ggcorrplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "sTU5RPTHiYUP",
    "outputId": "1854050c-f82e-4c0c-fea9-a4342fa2d9a6",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ggplot2\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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YIF4eHhHWzE6XQKIebNmzdt2rQbbrhh\n0KBBhw4dWrt2bUBAwDPPPNPxznT2nwkA4C89OWmeZEpKSp5++unx48dHRETodLrQ0NBJkya9\n/fbb9fX1TWo2NDS8+uqrY8aMMZvNAQEBKSkpjz76aHl5ubeCZ37XF198sfGnWixUVdXlcr31\n1lueW4rpdLq4uLg5c+YcOnTIW6HJBMX19fWPPfZYUlJSQEBAfHz83XffXVJSoqrq3LlzAwMD\n+/Xrt2/fPlVVn3vuuYiIiICAgDFjxqjNJiju8locOXJECJGWltbGlmy35Y5PUNyYRqOJjIyc\nMmXKkiVL6urqmtTv2mbp4Kda7FtrszR755DzTlDckaW4XK6lS5decsklERERWq02ODh44sSJ\nK1ascLvdHW/kvvvuE0KsXbt269atkyZNslgsgYGBl1566T//+c9Obagu/9MDAHxLUZmMAAAA\nQAqcYwcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJg\nBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJXW934ByzevXqPXv29HYvpOV2u91ut0aj\n0Wj4yYHu8uxOWq1WUZTe7gvOeS6XS1VVdie/uuGGGy666KLe7sU5j2DXOfv37x8/fnxycnJv\nd0RODoejrq7ObDYbDIbe7gvOeTabzWazWSwWnY4/dOiuuro6h8NhtVr52eknGzZsKCgoINh1\nH3/vOm3AgAGpqam93Qs52Wy2mpoai8ViNBp7uy8459XV1dXV1QUHB+v1+t7uC855NTU1Npst\nNDRUq9X2dl/ktG3btt7ugiT45QEAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACA\nJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYA\nAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg\n2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAA\nSIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAH\nAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJ\ngh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAA\ngCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2\nAAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACS\nINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEA\nAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJg\nBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEhC19sdAACgj7LZbLW1tfX19TabzeFw1NTUGI1Gs9kc\nGBio1Wp7u3dACwh2AAD8RFXVysrKoqKisrIyu91eo7WXaWrtitOpuI1lOrMaEOY0GxSd1WqN\njIyMjo42GAy93WXgJwQ7AACEEEJV1eLi4hMnTlTUVR0OOJMbcOa4pbRe09CsnujnsiY6wocf\n798/LyQ6OjohIcFkMvVGl4GmCHYAAIiampqcnJzT1SXbA4/tCDvp0DhbraqIIl1Vka7qW/Ox\n2IaQCWVJyWfOJCQkJCQkKIrSg10GWkCwAwCc706dOnXkyJEdhhNbw3PqlWZDdK0r1Fd8oN+Z\n6AiffrK2tLR0+PDhRqPRf/0E2kWwAwCc13744Ycj+XkbrftyDGe61sIxQ+mykH9dUzPSttOW\nlpZmsVh820Og4wh2AIDz19GjRw8X/rAqZEeRrrI77Tg0zg+DdlfX2tQ96ujRowMDA33VQ6BT\nCHYAgPNUfn7+kYK8/wv5d7Gu2gfNKeJzyyFtjUazV3PhhRdytSx6BRMUAwDOR1VVVXl5eeus\ne3yT6n70meXgYXfhoUOHfNgm0HEEOwDAeUdV1cOHD39jPJpnKPFxy0Jdb91bUFF0+vRp37YM\ndATBDgBw3ikoKCisL/nafMQfjddqHH8PzPnhhx9cLpc/2gfaQLADAJxf3G53fn7+PwKPOBW3\nnxaxJ6Cg0F1WWFjop/aB1pwDF0989dVXL7/8cuMSRVGsVuuwYcOuu+66YcOGCSH279//2GOP\nDRky5MUXX2w+P+Rf//rXd999d/r06VlZWU1a02q1QUFBAwcOTE9PnzJlitls7oE1AgD0orNn\nz55tqDhg9eehUkVkm4/FF0TEx8czazF60jkQ7DxSU1M9GU4I4XA4CgsLs7Ozs7OzFyxYcPnl\nl48YMeKqq6767LPPPv7448zMzMYfPHPmzOrVqyMiIubMmdO8NafTWVpaeuDAgT179qxdu/b+\n++9PS0vryfUCAPSwoqKi742nXMJfw3UeBwNOV9XUVlRUhIaG+nVBQGPnTLAbNWrUzJkzG5cc\nOHBg0aJFb7/99sSJE/V6/dy5c7/77rv33ntv/PjxERER3mpvvvmmw+G45557Gt/Ir0lrbrf7\nyy+/XLZs2e9+97vFixcPGTKkB9YIANDzXC5XRUXFoeCiFt91lNbmPLOlYkeBolHCMgYmL5yq\nDWw6a0ltXunRl76q3HtKUUT01cMGPzhZo9c2b8qpuH8wnB1cWkqwQ086h8+xGz58eFpaWk1N\nzbFjx4QQZrP5l7/8ZX19/bJly7x1/vWvf+3cuXPy5Mljx45toymNRnPFFVfcd999Doej8ccB\nAJKprKysE/YibVWL7x54eLPb5hz7zszRb99Sf7Ii5/dfNKngtjv3/vJDfbAxfc2ctDduKt9+\nIm/Jv1pb1nFDWXl5uS97D7TnHA52QoigoCAhhN1u97wcN27cxIkTs7Ozv/32WyFEXV3dW2+9\nFRwcPH/+/I60lpGRkZSUlJOTc+rUKf/1GQDQi2pra8/oqkVLp73Vnywv334yedFUc2JY4OCI\nwb+59Mynh51VtsZ1qg4U2Yuqkp+40hQbbL0gJuk3l576617Vrba4rCJtZV1dnaq2/C7gD+dw\nsHO5XDk5OYqixMXFeQvvuuuuoKCgZcuW1dXVvfvuu+Xl5Z6SDrY5evRoIUROTo5fegwA6G31\n9fWlmtoW36o6eMYQEWjsH+x5GTSin1DV6sPFjeuoTrdQFEX3n29PY3SQs9puK6hoscFyXZ3b\n7faOPgA94Jw5x64xh8Nx+vTp999/v6ioaNKkSY1PXwgODp43b96rr776/PPP79mzxzOG1/GW\nw8PDhRCVlT/dMfDs2bOffPKJ92VVVZXdbq+vr/fFeqApp9MphHA4HPzARfd5die73e55AnjY\n7Xab0tDiW86Ken3IT2dja/RardnQUPFff/CDhvXTWQzH3tg26N4MV73z5MrvhBANFTbTgBYa\ntClOoYja2lr+prWroaHB7fbv5SzniXMm2K1atWrVqlVNCseNG3fPPfc0Kbz88sv/8Y9/7Nq1\ny3PWXaeW4plMUqv96TTYM2fOLFmyxPty+PDhNputtrblX3vwCYfD4XA4ersXkITNZmu/Es4n\nDQ0NTqXj8wY3DWQ6i2H4i9ccfuKz/He+01mNg+7NKPr4oKJv+fCXKtQG4SLYdURDQwPzOfvE\nORPsRowYMXLkSM9zRVGCgoKGDRuWmJjYYuVrr712165dEyZM8IzAdZznDjBhYWHekgEDBjz3\n3HPel59++qnZbO74sV10SkNDg81mMxqNer2+t/uCc57D4bDb7WazufFPNUCv1+sbWv7u04eZ\nG8p/Gp9z253OWochIrBJtbCLB17yxS+cVTatJaD2aIkQIiC6lS8FVeiF1mKxBAY2bQRNGAwG\n/qv6xDkT7EaOHNlkupM2eHYOjaZzZxCqqrpjxw4hxPDhw72FVqt16tSp3pdfffWVXq8PCAjo\nVMvoIM+PWp1OxxZG93l+/ev1en4noDGj0WiqaXmXCBrez1FaW59fYYoPEUJU7jml0WuDkqMa\n11Gd7uLPc8InJOqsRiFE6b/yzIlhhrCWJ7c3qwahisDAQP6mtUun03X2WxstYiP+5LPPPjtz\n5sy4ceNCQkJ6uy8AAL8wmUzhrpbHz0yxweETEg8/+VndsbKaw8W5z30Zc/1Izzx2hat3n1r3\nvRBC0WmOL/v26MtfO0pqS7YePf7mtwnzLmptWWEus1arJdWhJxHshBBCVdVPP/30rbfeMpvN\nt99+e293BwDgLxaLJdoVpGlxvhMhhi2erg8z77zt/b33fBg8KnbIQ5d5ykv+mVf+7XHP8xEv\nXVv7Q+m2/1mW88yWxHsnxFw3orVl9XeGcBAWPeycORTrW3v27PGcnq+qamVl5ffff19cXBwc\nHPzoo4/Gxsb2du8AAP5itVqNwhDTEFyob2GOEn2IacRL1zYvT1t6o/d54OCIse/d2pFlDWwI\nD+3HbSfQo87TYHfo0KFDhw55npvN5tjY2CuuuGL69OkWi6V3OwYA8CuNRhMWFja8NqbFYOdD\nRlU/yBHR2Wv4gG46B4Ld5MmTJ0+e3KmPpKWlbdy40VetAQBk0q9fv+EH+m+15DYIP86vMcIW\nE2QMDA4O9t8igOY4xw4AcH6JiIiIMAaPqo9rv2pXaYXm4vpB8fHx/lsE0CKCHQDg/KIoSkJC\nwsS6wSbVX1PhpNclROtDY2Ji/NQ+0BqCHQDgvNOvX79+logpNSn+aDzEZZpUN2Tw4MFMzIae\nxz4HADjvKIqSmpo6tiFhhL2/b1vWqZoZVaPjo/pHRkb6tmWgIwh2AIDzkdlsTk5Onl41IqHB\ndxeuquK66rQhAf2Tk5N91ibQGQQ7AMB5Kjo6OiVp6M8qxyY6fJDttKpmRvWoUSIhLS2N256i\nt5wD050AAOAnAwYMUBTlZz8oXwYe/s50osvtWN3GG6pGJetj09LSuIcYehHBDgBwXouPjzeZ\nTKbDAYMcEX8LOlShqevUxxWhpNliL69NHhDWPzU1VafjixW9if0PAHC+i4iISE9PDzkSklgS\nscdYkG3Oq9DUt/spRSjJ9qgJdUnxSkTS0CQmN0FfQLADAEAEBASMGDEiriIu8nj4hWUDTujL\ncgxnjutLS3W1LuFuXNPk1sc5QxMd4an2fqEaS1xcXFxcnF7vrynxgE4h2AEA8B8hISGjRo2q\nr68fWFQ0sqysuqLapbqrNLZ6jUMVQqdqglSjya03GAwhISGRgyIjIiKYrA59CsEOAID/YjKZ\nEhMTExMTXS5XfX19fX19XV2d3W63Wq0BAQEmk8loNPZ2H4GWEewAAGiZVqu1WCwWi6WmpsZm\ns4WGhjKPCfo4BpABAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIE\nOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA\nAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRB\nsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAA\nkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEO\nAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEAS\nBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAA\nAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEmoRpsUAACAA\nSURBVAQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEAS\nBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAA\nAEkQ7AAAACRBsAMAAJI7c+bMI488MnLkyKCgoKCgoNTU1Pvuu+/IkSO93S/f0/V2BwAAAPzo\nm2++ufbaaysrK6dPnz5z5kwhxL59+954443ly5evXr16+vTpvd1BXyLYAQAAaZ05c+b6669X\nFGXbtm3jxo3zlh8+fHjq1KmzZs3KycmJjo7uxR76FodiAQCAtF577bWSkpIlS5Y0TnVCiJSU\nlHffffeJJ57QaP6ThT799NNJkyYFBQWZTKYRI0a8/PLLqqp63powYcKkSZM2b94cHx9/ySWX\ntFjSRzBiBwAApLVhw4awsLBbbrml+VuXX3755Zdf7nm+fv36GTNmXHXVVX/5y18sFssnn3xy\n//33FxUVvfDCC0KIgICAkpKSBx988NFHH01ISGixpI8g2AEAADmpqpqTkzNp0iStVtt2zUcf\nfTQ+Pn7Dhg0Gg0EIMWXKlLy8vFdfffXhhx8ODw9XFGXfvn3r1q274YYbPPWbl/QRHIoFAABy\nqqurc7lcVqu17WqnTp06fPjw1Vdf7Ul1Htdcc01DQ0N2drbnpcFgyMzMbPyp5iV9AcEOAADI\nyWw263S6srKytqsVFhYKIWJjYxsXxsTECCFOnTrleRkREaHX6xtXaF7SFxDsAACAnBRFGTZs\n2O7du+vr69uuJoRwu92NCz1XTngvrWie4fpgqhMEOwAAILEZM2bU1NT86U9/av7Wt99+m5KS\nkp2dHRcXJ34ct/PyvPS8dQ4h2AEAAGnde++9/fr1W7hw4ccff9y4fO/evTfddFNZWdnQoUP7\n9es3YsSIzZs322w2b4V169aZzeaLL764x7vcLVwVCwAApBUeHr5x48bp06dnZmZOmTJl4sSJ\nWq12z54969evj4iI+Nvf/hYWFiaEeP7556+55prrrrvunnvuMRgMGzdu/OyzzxYvXtzuhRd9\nDcEOAADILD09/dChQy+99NLmzZtffPFFjUYzaNCghQsX/upXv4qIiPDUufrqqz/77LOnn376\n1ltvdTqdw4YNW7Fixe233967Pe8Cgh0AAJBceHj44sWLFy9e3EadK6644oorrmjxrS1btrRb\n0kdwjh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAvpSTkzN+/HidrtW5R8rL\ny2fPnh0bGxseHp6ZmXn8+HFfLZpgBwAA4DMffPDBZZddlpyc3EaduXPnnjhx4pNPPsnOzrZa\nrZmZmS6XyydLJ9gBAAD4jN1uz87OvuGGG1qrkJ+fv2nTpiVLlqSlpQ0ZMmTp0qU5OTlbt271\nydIJdgAAAD4zZ86cAQMGtFFhx44dRqMxLS3N8zI0NDQ1NXX79u0+WTrBDgAAoOecPXs2LCxM\nURRvSWRkZHFxsU8a55ZiAABAQsccpd/XFzYu2Vl3Ymt1TqcaCdMF3hGe0bhEK5TpwSO72bfG\nqa61kq4h2AEAAAlVO2vvyPtj99v5V+W+xi9/Hp7RzWAXHR1dUlKiqqo3zBUXF0dHR3enTS+C\nHQAAkJBGUSyK7085C+j20Fp6errdbt+5c+eFF14ohCgpKTl06FBGRka7H+wIgh0AAJCQRgiT\nH64l0LfXZlFRkdPpLC0tFUIUFBQIIUJCQiwWy/Lly2tqahYsWNC/f/8ZM2ZkZWWtWLHCZDLd\nd999Y8aMmThxok+6x8UTAABAQopQAhSNzx960c6I3fjx4+Pj4++8806XyxUfHx8fH//2228L\nIb744otNmzZ56qxYsWLkyJHTpk3LyMgwGo0bNmzgHDsAAIBWaYQI8MOhWF17Cay120isXr3a\n+9xqta5cudJ3nfoJwQ4AAEhIEULvh2Cn9dHQmp8Q7AAAgIQUobR7PlwXaPv2aWwEOwAAICF/\njdi1d45d7yLYAQAACSmKwqFYAAAASej8EOw0jNgBAAD0MEUInV/OsSPYAQAA9CxFKHpF6/Nm\ntX4YBfQhgh0AAJCQ4qdDsZxjBwAA0OMUf4yuaZjuBAAAoIcpil9G7LgqFgAAoKcp/plMWOHi\nCQAAgB6mCIUROwAAAEkwjx0AAIAMFD9dPMF0JwAAAD3MT+fYMWIHAADQC/wz3QnBDgAAoGcp\niqL1w50nOBQLAADQC5juBAAAQAaKUHTcKxYAAEACCufYAQAAyMIv050oTFAMAADQw/w2Yseh\nWAAAgJ6mMI8dAACADBTFL1OTcCgWAACgpyn+uVestm8Huz59nBgAAKCrFI3Q+PyhtJedysvL\nZ8+eHRsbGx4enpmZefz48eZ10tLSlEYsFouv1plgBwAAJOS5eMLnj3bPsZs7d+6JEyc++eST\n7Oxsq9WamZnpcrma1CkrK3v99dfzf5Sbm+urteZQLAAAkJJfpjvRtHkoNj8/f9OmTbt27UpL\nSxNCLF26NCoqauvWrVOnTm1craysLCkpKS4uzvfd83mLAAAAvU4Rwj+HYtsKdjt27DAajZ5U\nJ4QIDQ1NTU3dvn174zp2u72urm7dunVjxoxJSEi48cYbfThiR7ADAABy6vlDsWfPng0LC2t8\n5WxkZGRxcXHjOlVVVdHR0Q6H480331yzZk19ff2kSZMqKip8ssocigUAABLSKtqM4NGNS4oc\nJSdspzrViEkTcIEluXFJ2yN2oqX5UJqUREZGFhUVeV9+8MEHMTExH3744bx58zrVtxYR7AAA\ngITcwv1d9f4mhZ2d2c6uNjRpJMkUP1gMaK1+dHR0SUmJqqreMFdcXBwdHd3GIoKCggYMGJCf\nn9+pjrWGQ7EAAEBCilC0QuPzR9u3FEtPT7fb7Tt37vS8LCkpOXToUEZGRuM6+/fvnz9/vsPh\n8Lysqak5efJkUlKST9aaETsAACAhv90rtq1Dsf37958xY0ZWVtaKFStMJtN99903ZsyYiRMn\nCiGWL19eU1OzYMGCmJiYjz76yOFwPPnkkw0NDQsXLgwLC7vxxht91D0AAAAZaRSNzx/t3lJs\nxYoVI0eOnDZtWkZGhtFo3LBhg+cjX3zxxaZNm4QQ4eHhW7ZsKSws9GQ+p9P59ddfm81mn6wy\nI3YAAEBCnkOxPm+23QmKrVbrypUrm5evXr3a+3zUqFFbtmzxbcc8CHYAAEBOPX8ottcR7AAA\ngJSUzl4D26FG/dCmDxHsAACAhBQheuVQbO8i2AGQjaqqDQ0NtbW1tbW1er3ebDbr9fre7hTO\nVaqqOhyO2tpam81mMBjMZrNOx1fnOULp9Kx1HWq1vYsnehd7JwBJuN3us2fPlpSUlJWVOZ1O\nRaMIjTh+/LiqqgEBAREREZGRkaGhob3dTZwbXC5XcXGxZ3dyu91CoygakZeXJ4QwmUwRERFR\nUVFWq7W3u4m2KELhHDsAOPeoqnrmzJljx47Z3Q5tiEE3yGQwaRW9RgghVKE2uF01zjMVZwv3\nnQoOsiYlJQUHB/d2l9F3ud3uU6dOHT9+3Klx6UINhqEWjVGj6DRCCNWtCqfqqm44VXEmv7Ag\nLCQ0KSnJYrH0dpfRqrYnE+4apW9PFUewA3Bua2hoOHDgQEVNpT7GaI4MafpbWhGKQaMLM+jC\nDAanWne6fvee3fFx8YMGDerjx1PQK+x2+759+2oddYY4kynM0GQnUTSKMCi68ABdeIDqcFed\nqv1ux3dJg5IGDGj1BlPoRYpQdIrW581q+/afDoIdgHNYfX393r177boG03CrZ0ylDYpOMcSb\ndeEB+UcLamtrhw8frtX6/o8+zl3V1dX79u1zBQrT0GBF096N3g2agIGBujBDXt6x2tralJQU\nfir0NUrn7wzbsWb79Ihdn+4cALTB6XTu3bu3weQyDW0/1XlpzFpTqrW8vvLw4cN+7R7OLTab\nbd++fWqY1phkaTfVeWmtelOq9UxZ8dGjR/3aPXSJohVanz/6+Dl2BDsA5yRVVffv3+/QNRgG\nBnb2z6yi1wQMtpwtLzlx4oR/eodzjNvt/v77790WYYgzdfazSoDGONhScKqwqKjIH31Dlyn+\nuqVYn85OPjsUu2LFivXr17dRITU19fnnn//qq69efvnlNqr95S9/sVqtnmp6vf7111+PjY1t\nUueuu+4ymUyvvfaat0RV1W3btn399ddHjhyprKw0GAzR0dGjR4/OzMyMiIho/NkWO6DT6cLD\nw9PS0m6++ebo6OgOrTCAXlVcXFxRXWEaEdLxwZXGNAZNQGLg8aPHo6OjjUajz7uHc0tBQUFt\nQ51paBevqtGYdQEJ5qNHj0ZERDAZSh+i+Oeq2L59zN1n+9+IESNcLpf35VdffVVdXT19+nSN\n5j/btF+/ft53hw4dmpyc3GI7BoPB+7yhoeHNN9/83e9+1/aiq6qqFi9efODAAaPReMEFF0RG\nRjocjiNHjqxbt27Tpk133333lClTmnwkNTV12LBh3pc1NTVHjhz5/PPP//Wvfz3//PMJCQkd\nW2kAvcPtdh87dszQ36Touv4XVmvVK1ZdXl5e478GOA85nc6TJ08GDDB37UeChy48oL7YfuLE\niaSkJB/2Dd2h+Ouq2PMj2I0bN27cuHHel3v37q2urr799tsbBzWvsWPHzpw5s902hw8fvnfv\n3q+++mry5Mmt1XG73Z5UN2nSpKysrKCgIO9bu3fvfumll15//XWr1Zqent74U6NGjWregY8+\n+ujPf/7ze++9t2jRonb7BqAXlZaW2pw2U0SIEKKsuPSF+xfv+WanotGMn3LJg394xGwJbFL/\nyPe5Sx5/Zf93+/QG/ciLRv36uQdiB8YJIQJiTcUHiwcPHtziXyqcJ06fPu3Suw2hBtG93Unf\n33jq2KnExETviAZ63Xk4j12f3vmuv/76qKio5cuX19TUtFbnm2++OXDgwMiRI++///7GqU4I\nMXr06IULFwoh3nrrLVVV213cNddco9PpDh061P2eA/Crs2fP6kIDPOMrj8971F5ve/OzFf+7\n8U8FeSdf+PWzTSrb6+3/77qshCED/7JtzRub36qrqX1i3kLPW4pJqwRoSktLe3oF0JeUlJTo\nQv+T7LuzO+msBpdwVVRU9Gjv0TpFKOfhOXZ9unNarfauu+6qrKx85513Wqvz97//XQgxc+bM\nFq8zHz58+AUXXFBUVHTw4MF2F6eqqmeG+u70GUAPKCsr04bohRAFefk7vv73Qy8vHDg0MWnY\n4Ht/9+vPP/ysuqKqceWaquqf3zd3wbP390+ITRo+5Ob5P8v9Psf7rjZET7A7nzmdzsrKSl2I\nQXR/d1KE1sru1LdohcbnD0bsuk5V1XHjxl100UWff/55axMT5ObmGgyG1NTU1hoZO3asEKIj\n8xp8+OGHLpdr1KhRXe4wgB7Q0NDQ0NCgMWqFEId2HwyPjogZ0N/z1rAxw1W3mrsvp3H98OiI\nWb+6TafXCSFKTp/d8M5HE/5nkvddjVlXX1/fg91H32Kz2VShKj7anRSTlt2p7/DbiF2fDna9\nc/HOqlWrVq1a1bx85syZzU99y8rK2rt379KlS1999dUms4m6XK7q6uqoqKg2ZhmNjIwUQpSX\nlzcu/P777xu/rKmpyc3NzcnJiYmJmTVrVuO3zpw5s2bNGu/LyspKm81WW1vbzhqiS5xOpxDC\nbrc3vhAHaKK+vl5RFM8dwyrLKoPDfrqSUW/Qmy3mitIWjoWdPVV8/cirnQ3Oq26etuiN33rL\nFZ1is9Xzn/q8VVVVpej+MwTT/d1JY9DUl7M7dYXD4XC73T5vVuuHAaw+PmLXO8EuJSUlJSWl\nxfLmhRERETNnzvzzn/+8YcOGGTNmNH5LURRFUdo+f87zbpNTWffv379///7GJcHBwTfffPP1\n11/f5ES9s2fPNj4QPHz4cLvdzg8yv/KMx/R2L9B31dbWCkV4/7Q2+fWsqmqLP6fDosL/8s2a\nUycKlz/3p4VzHnzpg//Ml6ToFJfLxX/q81Z9fb2i/WmP6e7upNU0NNjYnbrA6XT6/Ce93+48\nQbBrZvTo0R25Ktbr2muv3bp166pVqyZMmBAVFeUt12g0wcHBZWVlDoejtYvaiouLhRBhYWGN\nCxsPDTocjrvvvru6unratGlNUp0QYuDAgW+88Yb35dq1awMDA7mDuJ84HI76+nqTycQlimhD\nQEBAXl6ecKtCo4RGhJaX/DQe77DZ62rqwqLCm39Kq9MmpgxKTBmUMGTgjaOuydl7ODktRQih\nNqh6vZ7/1Oez/MJ8zxNf7E5uk8nE7tQFAQEBvp8CUPHLvWL9ERZ96NyYR1Gr1d59990PP/zw\nsmXLFi1a1Hj4LTU19dtvv927d2+TCU28du/eLYQYPnx4a40bDIb58+c/88wzy5Yt81xF25jF\nYmk8jcvGjRt1Op1er+/W+qAVnp9rWq2WLYw2eE69cDvcGqM2dcywsuLSgmP5cYnxQoh92/fq\nDfqhF/zXNJnf/O2fSx5/5f+2rdXqtEIIQ8B//WxwO9xGo5Fd7rwVGBioulTVrSoaxQe7U4M7\nIMDM7tQFWq3W5+eu+Wkeuz5+KLZPp87GUlJSrrzyyn//+9/ffvtt41A/depUIcT777/f4hDu\nwYMH9+3bN3DgwCFDhrTR+EUXXTR27Njs7Ozs7Gyf9xyAb2k0GrPZ7K5xCiH6J8RecsWEZ+79\n7fHcY7n7cv7w0POZs6/zTDz217fWbHz3IyHEsLEjSs+UPv/rZ/J/OHk859iLDyyOS4wflPqf\nWWTdNU6LxdKLq4PeZTQadTodu5Ok/HPxRN/OTn26c03cdtttwcHBTSalS09PT09P/+GHH555\n5pnKysrG9ffu3fvMM89oNJqsrKx2G58/f75er1+2bBnnRgB9X0REhLPC4Xn+1FvPhEWGZV11\n+69vvveC8aPue/YBT/m2z//5763ZQojQiND/3fhmft7JWy+++c4rbnM6nH9Y+7reoBdCCFW4\nKh1NbjyI84qiKOHh4T7ZnVSX6qpu8Fyxh75AEUKraH3+OF9uKdYpO3fubG3O4fT09NYmHLFY\nLLfffvurr74qhEhMTPSWP/DAAy+88MKOHTvuvPPOCy64ICoqqqGh4ejRo3l5eUaj8aGHHmrj\nOKxX//79r7/++rVr17733nt33XVXl1YLQA+JjIw8WZCvOtyKQRMcFvzMyhea13l57RLv8+S0\n1D9+/HbzOq4Kh1ZoQ0ND/dhX9HmRkZHFh4tFnFlolG7tTqV2k9EUGNj0ThXoPYp/binWpwfF\neifY5ebm5ubmtviW1WptYya5yy+//Msvv2wyWYnJZHryySezs7O3bt2am5u7e/dug8HQr1+/\nm2666dprrw0JCelgr2655ZatW7d+/PHHkydPHjp0aMdXB0APs1qtYSGhVadqAwZ240tUFfbC\nukEJ3ADqfBcRERFoDLQX2Qz9TV1vxa06TttSh7QwtwN6i+KnW4r17RG7duYKQROLFi2aMWPG\nmDFjersjcrLZbDU1NRaLxWg09nZf0NfV1NR8t+M749AgbVAXT1RvOG1TSlzjx48n2KG0tPT7\n/d+bhgUrxi7uDI6TdQF1ugsvvLCPz17bZy1fvjwkJOTGG2/0YZsu1VXjqvNhgx4Gjd6k6btf\nUvw5A3BOslgsiQMT7T/UuO1dmfvKVdXgPG1LTU0l1UEIER4e3j+mf31ulersymCHs8TuLm0Y\nNmwYqa5PUYSiVTQ+f7R7VWx5efns2bNjY2PDw8MzMzOPHz/etTpdw180AOeqgQMHRoZF2nKr\n3bbOZTtXhcN2tGbw4MGcXQevIUOGBJutttwq1dG5+x84S+yOk3XDhg3j7Lo+RxH+uaVYO9lp\n7ty5J06c+OSTT7Kzs61Wa2ZmZvOJOzpSp2vOjXnsAKBFqamp2tzcokNFAYkWbUj7x2RVt+os\nsjmL7MlDhvbv378HeohzhaIoF1xwwcGDB0sPlwUkBnbkEL/qVhsK6t2lDcOHDefa6j7JTxdP\ntDVil5+fv2nTpl27dqWlpQkhli5dGhUVtXXrVs/sbB2v02WM2AE4h2k0mpSUlMGDBjccq7Pl\nVrtqnK3VVN2qq8xhO1ClKVPT0tJIdWhOq9WOGDEiof8Ax5Fa29Fqd12ru5NwC+dZe/33FYZa\n7ZgxY5jipG/yXDzRw4did+zYYTQaPYlNCBEaGpqamrp9+/bO1ukyRuwAnPPi4uKioqKOHz9+\nOve00AlNiF5r0ikGjdAqaoNbbXC7a5yuygadRjcoITE2Npbz6tAaRVESExP79et37Nix4kPF\nwqDoQgwak1bRa4RWUR1utcHtqm5wVzkNesPQQUNjYmI4r64v6/kRu7Nnz4aFhTXeKyIjIz13\nN+1UnS4j2AGQgcFgGDp06KBBg8rKykpKSmwVtvr6epfLpdfrTUajxRIWMSAiJCSE72B0hMlk\nGjZs2JAhQ0pLS0tLS21lNpvN5nK5AgICDAaD1RoRMSjCarWyO/VxilC0zYNdFyYD6eQ/dPMd\no2slXUOwAyAPnU4XFRUVFRUlhKirq6urqwsODubGnegavV7fr1+/fv36CSFqampsNltoaKjn\nVsU4N6iq8NEVCf9Fo2kj6kVHR5eUlKiq6g1qxcXF0dHRna3T9d75pBUAAIA+x+3yw6Ot66bT\n09PtdvvOnTs9L0tKSg4dOpSRkdHZOl1GsAMAADJSVb8EO7WtYNe/f/8ZM2ZkZWXt3bs3Nzd3\nzpw5Y8aMmThxohBi+fLlr732Wtt1uo9gBwAApNQLwU4IsWLFipEjR06bNi0jI8NoNG7YsMFz\nyPWLL77YtGlT23W6j3PsAACApNo8bNpF7V1+YbVaV65c2bx89erV7dbpPoIdAACQkedQrM/5\nIyz6DsEOAABIyh/Brr1Dsb2LYAcAAGTkp+lOGLEDAADoBb1xjl3vItgBAAAZ+ekcOw7FAgAA\n9AK/BDtG7AAAAHoa59gBAADIQeWqWAAAAElwjh0AAIAcVP8cNnVzjh0AAEBPY8QOAABADkxQ\nDAAAIA9G7AAAAKSgcucJAAAAKTDdCQAAgCz8M2LHVbEAAAA9jRE7AAAAWTDdCQAAgBxU4Z/p\nTjgUCwAA0NMYsQMAAJCDKoSLiycAAABkwIgdAACANAh2AAAAMlCZxw4AAEAa59+Inaa3OwAA\nAOAHqircLt8/OhnsysvLZ8+eHRsbGx4enpmZefz48eZ10tLSlEYsFkuXV5pgBwAAJOV2+/6h\ndu5Q7Ny5c0+cOPHJJ59kZ2dbrdbMzExXs9n1ysrKXn/99fwf5ebmdnmNORQLAACk1PtXxebn\n52/atGnXrl1paWlCiKVLl0ZFRW3dunXq1KmNq5WVlSUlJcXFxXW/d4zYAQAAGXnmsfP5ozMX\nT+zYscNoNHpSnRAiNDQ0NTV1+/btjevY7fa6urp169aNGTMmISHhxhtvZMQOAADgvylCWEP+\nq8RuE/W1nWtEo23aiF7f8U+fPXs2LCxMURRvSWRkZHFxceM6VVVV0dHRDofjzTffVFX1t7/9\n7aRJkw4fPhwSEtKsvfYR7AAAgIxUIcJj/quk/KyoqepcI1pd00YMxjaqr1mz5tZbb/U8//rr\nr4UQjVOdR5OSyMjIoqIi78sPPvggJibmww8/nDdvXue6KoQg2AEAADmpbnF4V3cbsdU1bSQx\npY3qV1111Z49ezzPBw0aVFJSUlJSoqqqN8wVFxdHR0e30UJQUNCAAQPy8/O71l/OsQMAADLy\nTFDs+0db59gFBweP+JHZbE5PT7fb7Tt37vS8W1JScujQoYyMjMYf2b9///z58x0Oh+dlTU3N\nyZMnk5KSurbSjNgBAABJ9fZVsf37958xY0ZWVtaKFStMJtN99903ZsyYiRMnCiGWL19eU1Oz\nYMGCmJiYjz76yOFwPPnkkw0NDQsXLgwLC7vxxhu71jtG7AAAgJT8M2LXyXnsVqxYMXLkyGnT\npmVkZBiNxg0bNngOy37xxRebNm0SQoSHh2/ZsqWwsNCT+ZxO59dff202m7u2zozYAQAAGalC\nNJsK2Ac6ef9Zq9W6cuXK5uWrV6/2Ph81atSWLVu62S8Pgh0AAJBS709Q3PMIdgAAQEZqp0fX\nOtZs5w7F9jCCHQAAkJKq+mPEzu1uOjFdX0KwAwAAMmLEDgAAQBacYwcAACAHVagu34cwpc0J\ninsdwQ4AAEhIFarqn+lOOMcOAACgZ6l+uvMEI3YAAAA9TfXPxROcYwcAANDj/DHdicKIHQAA\nQE9T/TNi5482fYdgBwAA5OSPiydUDsUCAAD0NH+N2HEoFgAAoMf54xw7RuwAAAB6nKqq3FIM\nAABAEn648wSHYgEAAHqaKlQOxQIAAEhB9c/UJByKBQAA6HGq6nL6vlXmsQMAAOhpqlD9cI6d\nXy7I8B2CHQAAkJKf7hXLoVgAAIAepjKPHQAAgCRUfxyKZboTAACAHqcK4YcRO8GIHQAAQA9T\n/TNip/btETtNb3cAAADAD1Shut2+f3T+4omcnJzx48frdK2OppWXl8+ePTs2NjY8PDwzM/P4\n8eNdXmmCHQAAkJTb5YdH50YBP/jgg8suuyw5ObmNOnPnzj1x4sQnn3ySnZ1ttVozMzNdri4e\nROZQLAAAkJHqn4snOjliZ7fbs7Ozd+3a9X//938tVsjPz9+0adOuXbvS0tKEEEuXLo2Kitq6\ndevUqVO70DtG7AAAgJz8cii2k+fYzZkzZ8CAAW1U2LFjh9Fo9KQ6IURoaGhqaur27du7tsqM\n2AEAABkpin7shMYFrqIC18kfOteG0ay/IL1xiSY80gd9a+Ts2bNhYWGKonhLIiMji4uLu9Ya\nwQ4AAEhIEx1rvGHOfxU1OFRbfSdb0SiBQY0LFKOpjepr1qy59dZbPc+//vrrjIyMjiykcapr\nraSDCHYAAEBCSoBRG5vQwwu96qqr9uzZ43k+aNCgjnwkOjq6pKREVVVvlQKItgAAIABJREFU\nmCsuLo6Oju5aBwh2AAAAvhEcHBwcHNypj6Snp9vt9p07d1544YVCiJKSkkOHDnVwqK85Lp4A\nAADwl6KiooKCgtLSUiFEQUFBQUFBTU2NEGL58uWvvfaaEKJ///4zZszIysrau3dvbm7unDlz\nxowZM3HixK4tjmAHAADgL+PHj4+Pj7/zzjtdLld8fHx8fPzbb78thPjiiy82bdrkqbNixYqR\nI0dOmzYtIyPDaDRu2LCBc+wAAAD6nNZuI7F69Wrvc6vVunLlSp8sjhE7AAAASRDsAAAAJEGw\nAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQ\nBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4A\nAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIE\nOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA\nAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRB\nsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAA\nkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsA+P/t3XtY1HXe//E5MMMM59MA\nHkhARRFE0QC1otJMMw+VWWtte3XQTLO73a27TDGPZKa2lq3brevmtW7kb9uoLi07mFk4gCgi\nhwE5KMpZBGUQEAZm5vfH7E4shxFPDHx6Pv5ivvOd8c1cb5nXfD+HAQBBEOwAAAAEQbADAAAQ\nBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4A\nAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAE\nOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAA\nQRDs0LeYzWZ7lwBx0E64iWgn9AsO9i4AkLS2tlZUVFy4cKGxsdFkMsnlcmdnZ41GM3DgQAcH\nWhTXpqWlpby8vKam5sqVK5Z2cnV1tbSTTMZHWVybK1eulJeX19bWNjc3m0wmBwcHNzc3Pz8/\nPz8/qVRq7+qALlzlXfPw4cPvvvtu+yNSqdTNzW3UqFFz5swZNWpUd6dJJBIHBwdvb+8xY8bM\nmzfPz8+v/V1mszk5Ofmnn34qLCzU6/VKpdLPzy8yMnLmzJk+Pj42CrD8jQ4MDIyKipoyZYqT\nk5P1rs8///yjjz5avHjxAw880PkXeeyxx1Qq1d///nfrc86fP3/+/Pntz0lKStq8efOwYcPW\nrVvX/plxS1VVVRUWFqqVCn8vT9cBGqWDwtDWWt94paqivKSkJCQkxNfX1941ot8oLS0tLi52\nVasGe3m4qP0VDg6Gtra6hoayknMlJSWhoaGenp72rhH9g9lsLi4uLi0t9XRxHqLxclap5DKZ\noa3t0uWGM0VF586dGzVqlKurq73LBDrq0eWQ0NBQa4YzGAzl5eWpqampqakvv/zy5MmTuzxN\nIpE0NDQUFhZ+9913R44c2bhx45AhQyzH6+vrN2zYoNPpVCpVRESERqMxGAyFhYWJiYn79u1b\nsmTJlClTuiugra2ttrZWp9OdPHny008/feWVV8aMGXMjv7/VsWPH3n333cDAwDVr1pDqek1J\nScnZ4uKQgIH+Xr+83aodle7OzoM13lUX6/Jyc1taWgICAuxYJPqLwsLC81VVYYEB3m6/vN2q\nHZXuzk63+WpKqi9kZWWFhobyUQFXZTabdTpdfV3d2GFB7s6/vCM4SRw9XJyH+PueqajKyMgY\nPXo0HxXQ1/Qo2I0dO7bDxS2dThcXF/fXv/71rrvuUigU3Z0m+c+FtD179sTFxUkkEpPJZEl1\nsbGxixYtav9xJyMjY/Pmze+//76bm1tUVJSNAkwm0w8//LBjx45169Zt2LBh+PDh1/IrdyEr\nK2vjxo0DBw5cu3ati4vLDT4beqimpuZEevpHf92ZkpYmk8mmTZ78waa3Xf/z+kul0gHeniql\nIuvM6dOnTy9evFgul+fk5Ni3ZvRZFRUVebm5f9u5I/no0S7baYifr0qpzMvLKywsXLBgAe0E\nG86ePXv2zJm/7dxxJDW1czvJpNJhgwYoHRx0Op1CoXj66adpJ/Qd1znjJCwsbMyYMQ0NDcXF\nxbbPnDVrloODQ15enuWmVqvV6XSjR49+5ZVXOlzEjoyMXL58uUQi2blzp+05qjKZbOrUqb//\n/e8NBsOOHTuu71ewys/PX79+vY+Pz/r1693d3W/w2dBDJpOpqKho0zsb21pbf/5638EvPis6\nc2bJK691OM3T1eV4Ssrjjz8eHh5ulzrRL7S1tZ05c2bTxo2tBoONdvLz9Eg9kjR37lzaCTY0\nNzeXlJRsemdjS0uLjXa6zU9z+IeDc+bMoZ3Qp1z/VGJLLGtpabF9mtlsNpvNjo6OlpuHDh2S\nSCTz58/vctppWFhYREREVVVVbm7uVQu44447hg4dmp+fX1FRcc3V/0dxcfHq1avd3NzWr1/P\nFfXedPHixXNnzyanHv3z5ndGDh8eHhr6ztrVexM/v1RX1+FMT1fnHf/3fx0u4gLtnT9/vrqy\n8khKylXbydvddfv27RMnTrRLnegXKioqLl+q/VmbfNV20ri7b9++/c4777RLnUCXrjPYGY3G\n/Px8qVQ6ePBg22d+9tlnRqNx7NixlpsFBQVKpTI0NLS788ePHy+RSE6dOtWTMiIjIyUSSX5+\nfk/r/m/l5eVvvvmmSqWyXLG7vifB9amtra0oPefv6xt427/nz0VFjjWZTCezOw5nPPvkE8MD\nhzQ2NvZ6jeg3amtry0tLetJOC377ZFDAYNoJNtTW1paXlPaknRY9/dRAf7+mpqZerxHo1jXv\nJWEwGCorKxMSEqqqqmJjY9tf5crOzm5/ZkNDQ0FBQX5+/oABA5588kmJRGI0Gi9fvuzr6yuX\ny7t7fo1GI5FILl261JNivL29JRKJXq+/1t9CIpFUV1evXLlSr9cPHz7cy8uru9NKSkq2b99u\nvanX65uami5fvnwd/yLaa2hoaGps8m73yiuVSlcXl5rai51PdlY7WvYa4JVHlxobG5saG3va\nTioV7QQbmpqaGhou97ydLBvr0E43qKWlxWg02rsKEfQo2H3yySeffPJJh4PR0dEvvvhi+yM5\nOTkdZo+6u7vPmzfvoYcesozbSqVSqVRqe/6c5d4ebjdlaQIbMdGGH374YciQIdHR0V9//fV7\n7733v//7v12eVl9ff/DgQevNsLCw1tbWqw5Ao0t79+596aWXLD/v27dPJpV2GJE3m81djtHL\nZXKTyWQ2m3nlYdW+nRITE6+hneQyo9FIO6G99u30z3/+UyaV9fivE+10cxiNRpPJZO8qRNCj\nYBceHj569GjLz1Kp1NXVddSoUUFBQR1Oa78znMFgWLJkyeXLlx944AHrIgmZTObu7n7x4kWD\nwaBUKrv8t6qrqyUSiY1LaO1VVlZaT7a9V6TJZOpwgmWthlKpvHTpUlJS0uDBgzsv6ZVIJCEh\nIV9++aX15vvvv+/q6spsvOszf/782NhYy88mk+lcUcGF2hrrvc0tLZcbGvx8NZ0f2NLaqlQq\nZTIZrzys2rfTlStXLpSV9rSdDK2Ojo60E9pr3056vf5yTXXP/zqpVCra6capVCp2pL8pevQi\njh49usvQY4NSqVy4cGF8fPyOHTssa10tQkNDU1JSMjMzu5sLn5GRIZFIwsLCrvpPmM3m48eP\nW09Wq9USiaS+vr7zmU1NTQaDYcCAAe0Pjhw50rKk4w9/+ENFRcXevXsDAgI6z4FVKpWDBg2y\n3lQoFDKZ7PquEcLb29syei6RSEpKSm4LDDpffeF08dmhQYESiST5aJpSqRg7uov1ZRfrL6vV\naqlUyisPq/btVFhYGBjco3Yym82XLjfQTuigfTvl5OQEDx3ak3Yymkz1TU1OTk60042TyTpe\nJcX1uYVfsBMTEzN+/HjLVsbWg/fdd59EIklISOhyKD03NzcrKyswMLAnW9N9880358+fj46O\n9vDwkEgkQ4cOlUgkx44d6zzUaykgJCSky+dRqVRxcXEuLi5bt24tLCzs6a+HG+Pr6+vq4Tl1\n8r0L/uf3pwoLT2bn/M/ry5954gnLTlHbd/1t156PLWfmF589V1be1tbW1tZWVlZWVlZmMBjs\nWjv6HD8/P0dn12lTJl+1nXIKiqpragwGA+2E7vj5+Tmonabfd/V2OpmXr9fXX7lyhXZC33Fr\nvzlx4cKFCoVix44dV65csRyJioqKioo6ffp0fHx8h0UPmZmZ8fHxMpls0aJFtp/WbDYfOHBg\n586dTk5OzzzzjOXg0KFDQ0JCCgoK9u7d236c/tSpUx999JFMJps1a1Z3T+jn5/f6668bjcb1\n69fX1NR0dxpuIpVKNWDAgGXLlml8fO56YOaDj82/Y0L0u2+ts9z79XcHvz98WCKRGE2mmHvv\nmzdv3po1a/Lz8wMCAgICAk6cOGHP0tH3uLm5eXp5LV++3NdmOxla2+6a9sAjjzyyatUq2gnd\n8fHxcVSpV61801djq52utBgmz5g5Z86clStX0k7oO27tePbAgQMfeuihTz/9dM+ePc8//7zl\n4KuvvvrOO+8cP358wYIFERERvr6+ra2tRUVFZ86cUalUr732Wudx2JMnT1o+BpnNZr1en52d\nXV1d7e7u/sYbb1jHSaVS6auvvrpixYpPPvnk559/HjFihEKhKCsr0+l0crl86dKlgYGBNkqN\niIh47rnnduzYsX79+rffflulUt38lwP/bdiwYXq9Pi4u7pOg2+T/vVxm//9LkEgkbUaj7mzJ\nwYMHIyMjGeaAbSNHjjx+vGH16lWfBAySddVOhra27OJzP/30U0REBCM+sEEqlYaFhaWnp29Y\nHx80wLdDt1ja6UqLIfN0cWpqqo3duwC7uOUTFR977LEff/zxq6++uueeeyyDoWq1etWqVamp\nqT/++GNBQUFGRoZSqfT393/00Udnz55tGVftIC8vz/rdFU5OToMGDZo6deqDDz7Y4eu//P39\nt23b9uWXX6ampmq1WqPR6OXlNWXKlDlz5thOdRYzZ84sLi7+/vvv//SnPy1btow//beaXC4f\nM2ZMTk7OsVOFwQP8fdxdre/HJpP5gl5/pqLKUe0UMTqcVIerUiqVY8eOzc7OTi84HTzQ38vV\nxfpf2GgyVV28dLaq2tXNPSwsjP/auConJ6cxY8ZkZ2frGxuDBvh5uDhb72ozGitqLp47f0Hj\n69vdDB/Ajq6y+Qg6iIuLe+SRR8aNG2fvQsRhMpnKyspKSkrMJpOLWqVUOBha2y43XZHJ5UOG\nDBk0qOPVF8CGtra2kpKSsrIymVTiolY7yGWWdlI6OgYGBvr7+5Pq0HMGg+Hs2bOVlZUKudxZ\nrZLLZC2trZebrqjV6uDgYMuuq7hZdu3a5eHhMXfuXHsX0u+xtBh2JpPJbrvttoCAgLq6urq6\nuqamJg93zyBPT3d3d96Dca0cHByCg4MDAwMvXbpUV1fX3Nzs4+06zMPD1dWVdsK1UiqVISEh\nQ4cOvXjxol6vb2lp8dP4jvDw6DBYBPQpBDv0CVKp1NPTU61WNzQ0uLi4MMcRN0Imk3l7e6vV\n6qamJnd3d4VCYe+K0I/J5XKNRqNWq5ubmz09PZkZgj6OQS4AAABBEOwAAAAEQbADAAAQBMEO\nAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQ\nBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAA\nAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDs\nAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAE\nQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMA\nABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATB\nDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABA\nEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsA\nAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ\n7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAA\nBEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbAD\nAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABCE\ng70L6H8SExO1Wq29qxBTW1tba2urQqFwcKAzcaNaW1vb2tocHR1lMj7B4kYZDAaj0ahSqaRS\nqb1rEVNWVtb06dPtXYUIpGaz2d419CfHjh0rLS21dxXCOnv2rE6ni4iICAgIsHct6PcKCgoK\nCwujo6M1Go29a0G/l5mZWVZWFhsb6+rqau9ahBUZGRkUFGTvKvo9rotcm6ioqKioKHtXIazE\nxMSEhIQ5c+bMnj3b3rWg3/vwww8zMzOff/75mJgYe9eCfi8nJyczMzMuLo7kgT6OEQoAAABB\nEOwAAAAEQbBDH6JQKNzc3JRKpb0LgQiUSqWbmxsLcXBTqFQqNzc3uVxu70KAq2DxBAAAgCC4\nYgcAACAIgh0AAIAgCHYAAACCINgBAAAIgvVi6BPa2trS09OrqqqCg4NHjx5t73IAAOiXCHaw\nv5ycnK1bt1ZXV1tuRkZGvvHGGyqVyr5VoZ8yGo3JycmpqalVVVVqtTo8PHzatGmenp72rgsA\negPbncDOtFrttm3bnnzyyYkTJ54/f37nzp1nzpyZMmXKyy+/bO/S0P8UFRVt3bq1vr4+JCTE\naDTm5eU1NTWpVKpnnnnmgQcesHd1AHDLEexgT1qt9sMPP1y1atWwYcMsRxobG19++eXa2to9\ne/a4uLjYtzz0L0ePHn3//feffPLJadOmWTaSNRgM+/fvT0hIMBgM8+bNe+qpp+xdI/qNysrK\nAQMG2LsK4JrJV69ebe8a8CtlSXVr1qwZOnSo9aBSqXRyckpNTb3jjju8vLzsWB76F61Wu337\n9jfffHPChAky2b+Xhcnl8tDQ0LFjx6akpGRkZPj4+LRvNqA7ycnJK1ascHZ2HjFihL1rAa4N\nq2JhH9ZUFxwc3OEuDw8PBweHgQMH2qUw9EfWdgoJCel8b0hIyMqVK5VK5a5du/R6fe+Xh37H\n3d1doVDs3Llz37599q4FuDYEO9hBcnLy5s2b77333s6pTiKRJCUlxcTEqNXq3i8M/ZHtdrIY\nOXLkY4891tTU9N133/VmbeinwsLCVq1apVKpyHbodxiKhR3U19cnJSXl5OS4uLh0GOn46quv\n/vWvf5WWlu7fvz8nJ+f8+fNms9nd3Z2vckd3bLRTe0OHDt2/f39zc/N9993Xm+Whn/L19R01\napRWq01LS7PdWmazWSqV9mZtgA0EO9hBd38xDxw48Pnnn7/44osajaaxsTE3NzcrK+vQoUOJ\niYlHjx4NCQlh0wp01sM3YAcHB51OV1tbO3PmzF6uEP1CXV3d3r179+zZk5qa6ujoGBAQ0JPW\n0uv1y5cvDw8Pd3Nz6/2agc4IdrCPzn8xDxw4kJiYGB8fHxoaGhkZOX369NmzZ48cOdLDw6O5\nuXnChAmxsbH2rhp9i1arraioGDx4cA+zXXp6utFo5IodOsvIyIiLi6usrJRKpfn5+ePGjbOM\n7NtuLb1eHxcXN2HChEmTJtmpcKAjtjuBPel0ujVr1jQ3N99xxx1FRUXx8fG+vr72Lgr9Q3V1\n9aJFi2bNmvXss89ajljbaeHChbNmzer8kD/+8Y9RUVHz58/v3UrR16Wnp2/dunXp0qUxMTES\nieT8+fN+fn7tT+iytSypbtKkSXQU+hQWT8CerDOUtVrtPffcQ6pDz3l5ecnl8oKCAusR2xPe\nCwoKysrKpk+f3rtloq8rKyvbunXrypUrLalOIpF0SHWSrlqLVIc+i6FY9Aaz2Xzy5Mlvv/02\nKSmpuLjYxcXFw8PDcpd1pCMzM9P2DGWgPZlMdvz48dOnT8+dO9e6cV13A2d6vX7NmjVPPfVU\nWFiY/UpGX7Rhw4bbb7+9ywH6oqIirVZbXl7u5+c3aNAga2vJZLLdu3eT6tA3EexwyxUVFa1d\nu3bfvn0XLlwoKCg4efLkgQMHzpw5ExYW5uTkJLmW1WdAe2VlZTqdbuLEie1X1XRuJ8vFldjY\n2Dlz5tixWvRB1dXVu3bteuihhwICAtofLygo2Lhx48cff3zixInU1NTDhw/HxMQEBwdbWisj\nI2PGjBmkOvRNBDvcWmlpaW+//fbs2bNff/31Rx55ZO7cuSNGjCgtLc3JyUlKSho7dqzl0h3Z\nDtehubn5yJEjQUFBw4cPb3+8fTtxcQU2lJeXf/fdd56enuPHj7ccaWxs3LVr11/+8pcBAwYs\nXbr0d7/7XWtra2Zm5rlz5yZPnmxprUGDBj3++OP2rRzoDsEOt1BhYeHGjRtXrVo1adIky0Z0\nMpls4MCBU6dOra+vz87OPnr06J133tn5ut24ceN8fHzsXT76OhcXly+++MLNzW3ChAkd7rK2\nExdXYIOzs/PXX3+t0+m8vLzUavUPP/ywadOm0tLSpUuXPvvss/7+/k5OTuPHj09PTz916tTc\nuXPlcrmltexdONAtgh1uFZPJtGbNmocffnjixIkd7pLJZFFRUXq9Pisrq6Ki4u6777Yct34a\nZmcTdJCSkvLzzz8bDAYXFxdHR0fLQcs7cV1d3YMPPtj5IVxcwVU5ODgEBgampaUlJyd/9dVX\nOp1uypQpy5Yta38NWCqV1tTU5OfnP/7449bZnECfxW7+uFVOnDhRWVk5derU7k5YuHBhYWHh\nsWPHioqKhg0bZjkYFhbG9HZ0UFBQsGXLFoPBYLnp7+8/cuTIkJCQESNGDB8+PDk5ubGx0dnZ\nufMDaSdc1e233/7nP//5xIkTarV67Nixrq6unc+5cOFCZGSkXC7v/fKAa0Www62SlZWl0WiU\nSmV3J8jl8ueee27ZsmUpKSnWYAd0FhISkpCQUFRUVFBQkJ+fX1BQcPjw4cOHD1tPOHbs2D33\n3GO3+tDP+fj43H///d3dW1JSkpqaumHDht4sCbhuBDvcKvX19Xq93va3KI4aNcrX17eqqqo3\nC0N/pFQqR40aZZ3bdOnSJUvCy8/Pz8nJ+eCDD9zc3MaNG2ffItHHWfZdyszMvHz5skajmTBh\nQmBgoO2HXLp0acOGDU899VRQUFCv1AjcKIIdbhVvb++Ghobs7OyIiAgbp2k0GsviCaCDysrK\nhISEzMxMBweHSZMm/fa3v1WpVJa7PD09J0yYYFkzcfDgwW3btr311lvLly8n26E7RUVF27Zt\nKy0t9fLy0uv1LS0tCQkJMTExL7zwgre3t/W05ubm/fv3x8bGOjk5nThxYvfu3TNmzOhyEifQ\nN/GVYrhptFrtyZMnlyxZYrlEd+rUqddee23YsGGbNm2yMTdlwYIFTzzxxOTJk3uxUvQDaWlp\nW7ZsCQ8P9/T0TEtLq6urCwkJeeutt7oc3LdkO4VCQbZDl9LS0t57773f/OY3999/v6Ojo9Fo\nzMjI+Mc//nHmzBkvL6/Vq1dbL93t2rXryy+/tPzs4eGxePHizsu/gL6MVbG4aXbv3p2UlFRT\nUxMdHS2VSn18fHJzc/Py8i5fvnz77bd3+ZCsrKykpKQlS5ZYNkMBLLRa7fbt21esWPHoo49G\nR0dPmzYtNzf31KlTLi4uI0eO7Hx+cHCwRqNJSUlJTk6OjY11cXHp/ZrRZ13TvktBQUF+fn7B\nwcHTp09fvHgxI7Dodwh2uGkmTpxo+QYea7YLDw//6aefcnJy9Hp9ZGRkh50C6urq1q1bt2DB\nguDgYHvVjD5Iq9V++OGHa9asse5TrVAooqKiDhw4YDQau1skYcl2gYGBnfe0w6/Zte67pFar\nQ0JCxowZExgYyAdO9EcEO9w0crl80qRJ7bOdi4tLREREampqTk5Oenq6v7+/n5+fZaA2IyMj\nPj5+xowZ06ZNs3fh6EOsqa5D3FepVNnZ2Q4ODtZdDzsLDg4ODw+/9TWiP0lPT//mm29ee+21\n7iaEREZGnjhxIjs7OyoqysvLq5fLA246Po7gZlIqlcuXL3/rrbcOHjwokUheeumlYcOGbdmy\nZdu2bZmZmStXrnR1ddVoNLW1tWazedGiRXfddZe9S0YfkpycvHnz5lmzZnW+iGs2m6uqqh5+\n+GG7FIb+i32X8GtDsMNN1jnb+fr6rlu3Li8vLyUlpbKyUq1WT5s27e6772YxLDpwd3dXKBRf\nfPGFRqOZNWtW+7s+++wztVptY7MxoEvsu4RfG4ZicZ3S0tLS09PoyhExAAAF3UlEQVTlcrmH\nh0eHv5idx2SlUqlGo4mMjIyNjZ04ceLw4cMVCoW9Kkef1f77gl1cXKxz7A4cOLB///61a9ey\nKgLXqrCw8OTJk+Hh4X5+fjZOS0lJ8fb2jo6O7rXCgFuEYIfrkZycvHHjxvT09G+//faLL77I\nzs4+f/682Wz28PCwTDfuMtvZu2r0A52z3YEDBxITE+Pj4zUajb2rQ//j6Oj4/fffl5SU3Hff\nfTa+6XXv3r2TJ09mDSwEQLDD9aivr09KSjIajVOnTvXy8iooKDh+/PihQ4cSExPT09PLy8tb\nW1s9PT0nT5589uzZ9nug2Ltw9APts11JSYlWq42Pj/f19bV3XeiX2HcJvzYEO1yboqIiT09P\ny1vvkSNHKisrly5dumDBgjvvvDMoKEitVpeWlqanp//888+ff/55SkrKoEGDLl26lJubW1NT\nExMTY+/y0T9Ys11xcfHMmTPZIRY3gn2X8KtCsMM10Gq1W7ZsufPOO52dnS1vvYcOHfrxxx8j\nIiKCg4OHDRs2ceLE2bNnT506dfjw4e7u7jU1NRkZGVeuXJFIJDExMaNHj7b3b4B+w5rtMjMz\n28+3A66Vs7Mz+y7h14OvFENPdbnBmE6nW7NmjUwmW7t2bUhISOdHNTQ05ObmNjY23nvvvb1Y\nLARhabDm5uaFCxd2WCcLXJPq6mrLvksSiYR9lyAwgh16pLttYyU9yHbAjSDb4SZqv+9SaGgo\n+y5BPAQ7XJ2NVGdBtsMtZc12mzZtYkwWAGxgjh2uortU19zcnJCQEBYWJpPJLNOhDh8+fPjw\n4YiICG9vb3tVCyFZGmzQoEGxsbH2rgUA+jSu2MEWG6lu9erVQ4YMeeGFF6ybmHDdDgAA++p2\nt0bA8sWd9957b09SnUQiCQsLW7VqlclkevPNN2tra3u9XgAAfu3YjBFda25u7vKLO7tLdRaW\nbJeXl8doLAAAvY+hWHRBq9V+/PHH27ZtO3XqVPsFibZTnV6vr6mpGTp0qF1qBgAALJ5AR5Z5\ndStWrPD29m7/5U4KhSIhIcFGqouLi3N0dBw1apRdygYAAFyxw3+xsQtxc3NzaGjo22+/3V2q\nmzRp0vz583u3XgAA8AsWT+AXWq32gw8+6LwG1jJzTqVS5eXl7d+/v8OjSHUAAPQRBDv8m1ar\n3bx5c2Njo06n63yvNdvt3Llz37591uOkOgAA+g7m2EEi+c8I7NNPP52Tk5OWltbld663n29n\nOYFUBwBAn0Kwwy/z6qKjoztEtw5nts92Mpls9+7dpDoAAPoOgt2vXUZGxrZt26zz6jpflutw\nvvWEjIyMGTNmkOoAAOg7WBX7a9fU1HThwoUhQ4a0P2hdBmvZvq7zo3Q6XV5e3qOPPtpbZQIA\ngKsj2KFrV812AACgr2FVLLrW3TJYAADQZxHs0C2yHQAA/QvBDraQ7QAA6EcIdrgKa7ZraGiw\ndy0AAMAWFk+gRy5cuKDRaOxdBQAAsIVgBwAAIAiGYgEAAARBsAMAABAEwQ4AAEAQBDsAAABB\nEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAA\nAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGw\nAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQ\nBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4A\nAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAE\nOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAA\nQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAATx/wGoUdNjSui1SgAAAABJRU5ErkJg\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggcorrplot)\n",
    "\n",
    "corr <- round(cor(M), 1) # Pearson Correlation\n",
    "ggcorrplot(round(cor(M), 1),\n",
    "  hc.order = TRUE,\n",
    "  type = \"lower\",\n",
    "  lab = TRUE,\n",
    "  lab_size = 3,\n",
    "  method = \"circle\",\n",
    "  colors= c(\"tomato2\", \"white\", \"springgreen3\"),\n",
    "  title = \"Correlation of Data Nasabah\",\n",
    "  ggtheme = theme_bw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3qhweqGyXRAP"
   },
   "source": [
    "melihat korelasi adalah dengan metode korelasi pearson dan korelasi kendall. Jika nilai korelasinya bernilai positif maka korelasi antar variabel tersebut berkorelasi kuat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M46clqhgjfsp"
   },
   "source": [
    "## Pemilihan Variable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SkRtLO5LXRAP"
   },
   "source": [
    "### Pemilihan fitur/independent variabel/input\n",
    "Dalam melakukan pemodelan tentu kita perlu meninjau variabel-variabel apa saja yang berpengaruh pada model kita, khususnya pada klasifikasi. Pada kesempatan ini kita menggunakan model Regresi Multinomial.\n",
    "\n",
    "Lalu bagaimana menentukan variabel apa saja yang berpengaruh tersebut?\n",
    "\n",
    "Ada banyak alternatif, salah satunya ialah Information Gain. Melalui information gain diambil nilai importance variabel yang lebih dari 0.02 (kamu dapat eksplorasi apa yang terjadi apabila kita mengambil nilai yang kurang dari 0.02).\n",
    "Berikut information gainnya:\n",
    "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqIAAACICAYAAAA8qYmKAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB4rSURBVHhe7d1PbuNIlsfx14NCozezqU2JlYYvMOgBLAPWAWbnWlC5mhNYKRf6CmlBeYZBSakbzCpNIO07yIAkIBtzASNtqja1bhQK0LwXDFJBWpSVsrNkO78fQFUmQ/wnRkI/RQTJvyyUAAAAAH+yf/P/BwAAAP5UBFEAAADsBEEUAAAAO1E7RvTXX3+V3377Tf744w8/BwAAANjcd999J99//7388MMPfk7ZyiBqIfRf//qXRFEkf/3rX/1cAAAAYHO///67pGkqf/vb31aG0ZVd89YSSggFAADAQ1iWtExp2XKVlUHUuuMJoQAAAHgoy5R1Qz25WAkAAAA7QRAFAADAThBEAQAAsBMEUQAAAOwEQRQAAAA7QRAFAADAThBEAQAAsBMEUQAAAOwEQRTP3/xSep2O9C5SPwMAADwHBFE8sqkMOz25nPvJknVlL0N60ZPO+6mfAgAA6xBE8fw1jqU/Gkn/p8jPAAAAz8FfFsr/XfjnP/8pf//73/0UUDZ935HhxE+o5ulIugd352ea0h11RdaUNfUvW/bqSN83GErWnrgsWyfcZr4fxlomh9KSKDmXaaMt3dZYhkkqUdzXwJrKsHMlR6ciw4FvvTzsyuiN35p19Z+dS97Rny2Thdza/awskyuWrZbrPvXfHYut1a1zry2p7qsrD8qc2VA6+X6q8DhLn3l1OQAAnojabGlBtOrTp0/+L+Ae6cXi7O3F4tZPLhaTxeDkbHGR+smS+rLJ8GRxcjLQdyynzz4u17re7eLi7cliMPWT6vbjma7PtmXb1HUPJ9k8/X+2Hzqv2O9gv+x4SvuYvTdf9337udzGerZceZ35NivHMh0EZWW2req2N//MAAD489RlS7rm8cXcOMhOJ3tZK9/8+k5L4DaiOC5aQJtvHqGr/TCW44b9EUk7rrat6ryf89bDphwdpnJtB5HqsRTLGSvT2bfLI9xuP218rP/M9GWtmOV1dot9bbxalk2vpkFZKJXZOJU0WZ6LXqLTnx/jTAAA8OcgiOLLzC9lmIi0341kNNLXuzZdwRuYvh/K1Lr/7TPTV1fD7cNpmM7PQ/7KhxcAAPAMEETxZazF0FrtfAvdNB/XWIhkv+FbF+9YV7ZDGq6TSVOObNxltC/RJFle2e/KImkdbha3ox/1fTfzymeSyvxGy/b8Otw6sz/v0zxqSpokfjxqyFpOUzn/5bKyLQAAng+CKL7MQSztxrKbOdlrF93UmUiOXzdlOsi7ofOLesy6sm2kcnlm6+nJuQbHfL3DmS9eS0OcW1ZfZ2NpvfMXRtkV+KdRUHYu0Wl/Rdd4Dft85Nzd19SWz+5tmh130Y1+di2teMN25IOu9GO7uMrvj77y42u+6Ze2FZYBAPAccNU8vkEWpBPZf/cFARMAAGytLlvSIgoAAICdoEUUT5i1XNZ139uFOtu2aNIiCgDAn6kuWxJEAQAA8FXRNQ8AAIAnhSAKAACAnSCIAgAAYCcIogAAANgJgigAAAB2giAKAACAnSCIAgAAYCe2vI9o5Ybg80vpuWdyj6R7YOX2DPDs+d9Ooy39d8cS5TcoL6bVbCidD/s63ZCkcvPyKO5L/6fsmdzT9x0ZTtyfhWbd9oKbna9fbg1/TPak8ExTuiP/PHIn32Y438971ZXRm2xOtv3sPdFFT3rJco0mO0a5uy63/WuJq+tetb21yz0hpc/0S25IX76xfXj+que3dG6tbg2CGnW4PC+PU1Y9hvJ+hvW3kH8G4TqdcNmH3KwfAICnpzZbWhCt+vTpk/+rzmQxODlbXKT29+3i4u3JYjB1Bc5keLI4GU78VDidLXcWvn86WJy8vdC1hOs0Nn2yOPt466cztq5N5lVt8p56ti8D/W8gvVic6THZepfHbp/FYDEY5u/V5fTv6rK3H89Kn0/+GZ691c8m30dbf7hc7fbuWe7JqJzfjfezWr+q9SRQ1CVTPWe2nny5LcvcPtcdgz8XRR2r7rfJ9n0wrJx/t57qewEAeDnqsuWDu+an73sybvWXrVDzS0msBTBo7WnGbYkmV0VLUet1W9KroMVpJV3HaVPS8cy3oNVJZX7j//wTpZOxREdNaeprWhxLKtdzkaMjkauZTs6usokNRa9jiWqOd/X2MuuWeyrSi0Smh3HRymfHk2qNcJ/TOrNEzqUtcdHKafUolfHk7tGmtzrvVSNradf/7jemMnzvPytbzzyShtv+lmXptaTBMUjjQFoNrX+uJd7OfVPiogU0koNWlO2TN30/lDTuSrznZ3j2WUgc/BsCAOAb8bAgOhnKULrl7kf7sm7s+zDg6be4fiX7L2wV6Rf4TSKXRVd6jUjXM9f1+cnVIjn+uS2S9KTT6cjwvmDzKFKZjSM5suBwcCTNIGQ7Ok80LE4thx5YsAmOfa2mHL06l+TOMdyzvdrlno70cyrRXlYrrDu99zmW7qHOD4LaKmG4TC/0HH/Yl26stUnXl7Eu7Y47970kCn4Aab14N9LaOXRlbvhHMVxhyzKrj+Fn70KqBlC3K1mAXQbrqSRJutzPmf5buWlLt9pV786tfjYyk54/jk5liAoAAC/VA4JoKufJtl+X+mX/OnrA8hWNYw0LIxlpYJCBfZH37g+5D+Fa6fKwrSHwsNqy15R4L5FkL/bhZnPWepx+uCyH73u3V7Pck2NjWjv6ufQr4yPvl4fXUT62uGDjY+3c6+tUgnGW2bbsh5KVdTWo9x5apvWsG6dF8O1c7Uu7oQHU7VD2gyh19c9eV7KvgTkL3xqWByLdO/u+NB1ruT+Ovm0jb5EFAOAFe0AQzS6oaN8MpXcRxJ9VrZjzuU7nXZ/eQazLVlv2Kla1rq6VhZJ+LI8XcldwXeNzCyhZ6LCLZbJ5epx+f6OfVlyosgkNO/GrscyCD7B2e6EVyz0lFsjSZCjXr/X8uM8lG1IR/bj+M3Llk2EpvIatqyVWp/LW5/lMxnYRl1+m+Ubrat5iuW2ZsvPqQq+93jTkuujSV8UPInt1pWH7qfvvhiVYGPXnz12wpscU/mBqvl6GVHfMN/ZvBgCAl+2BY0StG7MrUdJbdom7cXNTSYpwmsrlL+cicbV10MbQpZJ8qPm6tauLB9PSF/Smlt22DzC7XLaq2rjEIhBrKJlYCM8DRxY67naXb695FMn5h7Gf2nx75eWeluiwpZ9fMxteYFy3djBt7Ip0C2rhDxsbiqBLtg59LXBjkIPpUDie0wmHRGTjd5e2LctZy6mN+Vzd6m3DCKwr3sa2lsKrvvqx7rtdNT+yK+OzsaThDwv7O2odfHG9BwDguXnU2zfZBRdZa5eVB+PcilvVVJbL3+du51S9fVP5Fjb1t2GqbMsEt8apX85P1LKgYbdGsr+DfbGw5G43FQbk7L3j//gvkf/790oXbFZmLYHx7frbN9l7sv3Kt92UrnU3122v9Q9pjf9n9XL5uManpHTroxX76Mvv3PYov+WRmwjrReXch7cFM6Xt5Z+1L92qLP98s/nVemThszi/d27PtOTeZ8MMivLyetctCwDAc1SXLbcMogAAAMBm6rLlg2/fBAAAAGzjm20RXdVln7vTNQwAAICt0TUPAACAnaBrHgAAAE8KQRQAAAA7QRAFAADAThBEAQAAsBMEUQAAAOwEQRQAAAA7sf3tm0qPXVT54xWr83Olxy+Gj/b089YuZ4//DB8NquwxjMWjLyuPSCw9BhJPRu2jOu+zPL937/Fafsxn6bGbpUd1htur1hcV1MXqPWY3W6cpr7e6r6X1rnqMZ/758IhPAMALU5stLYhWffr0yf9VZ7IYnJwtLlI/WWMyPFmcfbz1U4HpQOdPFhdvV6/j7nIrtqfrOHl7sbB31W4HT0jlHKYXi7OTgc69jy13shhMb7W+VM9zNm8w9ZPhNtz667ZXXW6NoJ6tX6eu9ePZcv8q77Wyk+HyaOvq+GBYfh8AAC9BXbbcrmt+PvetWtuZXqXSOmzKQUtkPHnImkwq8xv/J56s9CKR6WFctB6mk7GeualczbLpek3pjoIWydAskXNpS1y0Vl7pGtOsTqXXkgbbk8aBtBpaV8JW0A2kt7quVw3XUnrfOqOfghZQX3btqvdUkkSkHeetnHrcE13deFb8O5q+H0oadyXe8zMAAPgGbBdEG8fSjUXOzzrSCbpFN6NfwjctOdAv8+jQkujyy3g7kRz/3BZJerovHRneG2ywC+nnVKK9LKRZF3XvcyzdQx/0thSGxPRCz/+Hfa2XkduWRPsSTSyYehZa53kwzEwHVn+zV7neWHd/Nr+XRNLNu8k3WGdhPpPxvClHFpLdD7dIGhZgrfvdhpmctiWaa7C1986GMrxpS5fHygIAvjFbX6xkrT+j0UhGp5J9aZ9dbhQoXctY3sJkrUYyltkXtlLdocG4b/sy6oq4cNGTy4euE1+BjaHsSLKndecRx0DmwXZUjEFW7sdSWgTKztW+tBuR7Ls36I+Xd1Zf/Ou0qaE0rDNZK+yyfvsfW2vXGdIge3Yu0WlX1xSw8aVn1xKP+nJcLKPvHYh0w30HAOAb8fCr5g+6+oXdl7acS3Jva2Qqs7HG1Yl+Ibsvc7uww3elbiMPtIUsQPSttTb5snZafF3WGpomQ7l+refHtfxlQyqiH7ePX25ZrUthsA1bXosfS/Z605DruW+VrDo4KgfG0EGsYbPc/b5+ndnFUxJe4KRviGy+XVynP5bctqybv7Ev39kPM7dMFm57Sf7vgx9TAICX75Fu35TqF7L/cx3fXVm0NtnrXXuD7vlI9jUMLANrKpcfpkXgqHJds3hSbBhGpBHMdVUb160dTBtrMbQwdrHh+XMBMpLWoa8H80tJJsF0wVpibQxmvDJwulb6RjZc5A63n6sC7Ip1um73Sgh19DgP9TNoHejeGl9/dfo/w2Crr36s77Cr5q3VdNX+AADwgmx3+6bSLWwypVvceNZlaq1V+QUcNo7PdaGWumXvtiBVl8tk7yu2WtzipjLfcPubp6lUb+wHyYquay0v3/bIAl/lVkuqqG+1t4QqL1eun5V1rrq1mPtblcrWrTOrt+Ftn5xi+fKyd29DlVn9bwQAgOetLltufx9RAAAAYAN12fKRuuYBAACAL0MQBQAAwE4QRAEAALATBFEAAADsBEEUAAAAO0EQBQAAwE4QRAEAALATBFEAAADsBEEUAAAAO7Hlk5XsMYiJ7OePU/SPWYzyRx7WPQJUdP6H/eCRicYefTgU+Tl/NKN/xGLp0Yqqus6gfNWjFfPHL94pq6535fbyxzEGj6F0x3gtcfWxlNhc7eM471N+7Gb50ZpryjbYnnukZpJW1rl8HOfaR3HeWc7Lt7vqUbMry8rHULdNAACeq6/4ZCX90v4lCKE5C3ajkYz8686XdZ3ZlaRxV9oylln+LHBzoF/c+fretYMgqeHjjd/GYfYlXt1ePm806ut6zyWZ+QJTtz0VNVJJLrIYg4fSsHU2lta7/By2ZKw/QMo/V1bxP1S0fmXnsCsy6MmlO1f3lP2y3F4/Fjn/5VLnBvTHTW8cSbMUTi0U9uT6tdaVupC8crlcdpzR4aoguaosO4Y0qKOtcU+GYR0FAOCFenAQnb7vybjV3yxoRvulALkUScN/qU+vUmkdNuWgJTKefI0QGMl+sBPrthe9jiUaz8rhBVtJLxKZHsZFi2Q6GevnOpWr+wLXLNGfDm2J8/qlPxymuqQ7V+vK9DwfBy2g0WFLovl1cC41FA5Saf8cy76fk7FW8HU/nOqWy0zfW6jsSrznZwRWl6VyPW9KXLSARloXI0lvqXUAgJfvYUF0MpShdFd3I87PpdfpSMe/ihYeHwasy7znWhvti9iVKA0mNy050PBgwUEeKQSmSc/vh4XmbtA9e9/2mnL0qtKCiq2kn1OJ9rJ64s7959i1YN8XuFz5q4b7AWPd4Ta0oxtrUNP1rSurcsH38KgYVpGHws2GBiytXW6m/x5u2tJd9e+htkx/GDXCQD6VJNFjW3EMAAC8NA8IoqmcJ2s6Vld1zTey0GDLzvead1obXauZDxbSOJDWiu7ybYRd89btmQXgzbbXjNuSfqh06WJL1g3dkWRPz0d17OQ98vA6Ko3vzawrc6wrPYmkm2/TQmHdD6h11i5nLaUi3ZX7sK4skuOftY4N8h9tV7KvYToP7QAAvGQPCKLZxR/tG/2S/9JxlPOZXEssR6988GtYl30qs7GuZzIsWi/P53k362Px3Z6utWnD7TWOJbb9fMzd+AZZsEqToVy/Hvkgpz9GbnT+j+sDlyvXcxSG17x1dV1ZQcNjx0JgcZGZhuEP+gOqdN41KloQPFv3g2P9cp/sR40FTlemwTjJ61ZP/vd/68vceFatY8sfbV1p2DHc87kAAPASPHCMqI3D60qUbHpxhXVDpnKVXMv+YSTNo0jGyVX25a/hdOyuUl+2otpFSXl3eXpxqV/lGdfNmrdkfpEsfLqgcs/2Qraf5x/GfgrbcGM0bahDMZ4z0SAXTBsLjRbUwh82B9adHkkrv8BnfinJxE+vK1Ouu74UQo3V2eCc2wVsDT3HdsFTXYuqs365//wpb3XPXv1Y12RXxuv7/vu/68uqXfy2z9aFX4x7BQDgBXvwxUo2jrKrAc66FksBom6MqMa86c2+G5dpQSKaTF2olMoYPse68nU9NkYz+qkhV35dpW5WZV2zbhsTXbsfDxoG4/IY0ezWONUxg06wvZKDWPQI7wRUfAFr9TuVolXwbkCsY/XLrrD3y7kr7/MAt64sG2vpusXzberr/tb7bPhA3uK5qj49NheY8/1bN8QAAIAXZsv7iAIAAACb+Yr3EQUAAAC+HEEUAAAAO0EQBQAAwE4QRAEAALATBFEAAADsBEEUAAAAO0EQBQAAwE4QRAEAALATBFEAAADsBEEUAAAAO7H9Iz7nl9I7Oy+evx7F2TPcM/a87uxZ3ZlI2vkzwN1y1xJv9JxxvCilOhPUiXvZ8+KH+t9M83Qk3QM/sbZMzYbSGfjSRlv64XPc68rC+ZX9nL7vyHCS/W2q2yuVH3Zl9GZZy+2Z8j17/n11P9S6MgAAnrvabGlBtOrTp0/+rzqTxeDkZDGY+snK9GR4sjj7eJtNVKUXi7OTgS6Bb4vVkbPFReonN64Ht4uLt9W6lq9nXZmWfjxbnNRso7bM7deG+zkdLE7eXuheZNw6h8t3hv8O7G8rc+8JljHrygAAeAnqsuVWXfPpRSLTw27QEtSUOI5kemWtSKnMb7K5QC6rM3HRsphOxlpTpnI1y6ZrzRI5l7bEeV2bXelSqYwn6foy/f9sLNJ+t6rlfU1Zei1psJ/SOJBWQ+t00bq/lN7qdl41fOvlVJJE1xnna9Rjm+h7xjPdmv4LeTMqtY6G1pUBAPCSbRdEP6cS7ZU7D6Mfdfpmrl+6kRz/3BZJetLpdGR4X9DANyGsM9Z93fscS/fQh7k1wrBn3dedD/vS1R89tr51ZTKfyXiuJZOsHrrXe9/dvq4s2pdoYoHWs7A7T+W62E0bCpAt00si6eYBUpOq1f1GPvykk8j+aVuiuQbb7B0AAKDi61ys1DiW/mgko1FXZGBf2j25XNGihG+NjR3uSLLX/+IWwDy8jlaMn6wvm8pYuloPrS72pX0zDH4Y1ZRp3e3GaRE2O1f70m5Esl+suCldt4y+TqU0PtWx8aVuDHRfjqs7CgAASrYKotay5VqdAuVuylz2pd2PRc6T0tc1vjGuziRDuX6t9cFd1JYN4XAt6Wu48smwFF7z1tV1ZZmmxMUFdJE0Xvl66tSXRT/p+vKw+aYh13Pf0ll1EGtI9d32+obIWks/7OuPMN/lb938jf3KvwkAAJDbLogetiSaJMtWzvmlDEvj48qqoRXfHldnNJ4dFeM5rcs7mDbWmmhd3hdBfTk40qUiaR36OKd1LZn46XVlbmxnOAbVxmxuUFZiLbhDSeM4C5ZV7hjykKrHcqjH2TrwwVOX/TANpgEAQNX2t28q3eJGv4aL29iUb6fjhLexKd3Cx6vc5gYvVKnOWGt55WIhX16+FZgq1ZnKbZ82LgvrqKotK996rHx7pkrdvnOrpfKy4XEUt2cK+Xq/rgwAgJegLltuH0QBAACADdRly69zsRIAAABwD4IoAAAAdoIgCgAAgJ0giAIAAGAnCKIAAADYCYIoAAAAdoIgCgAAgJ0giAIAAGAnCKIAAADYiS2frGSPOkxkP3+con9cYlTzmMTSoxDde68lrj7e0VQeG2ryRyxO33ck2csfmejX/6oro3heelRjwW8z1eWGEz/PC9dZKgv2c9VjF5ePbKwcf6B+ufTuo0+d8FGX+ed29/GX1X0tP3rymVj3OM61yo/WLB17tc6seDRmfk7qlyvvyyaf9cp1qtKypX2pPvp2xSNO88+Hx3sCAF6Y2mxpQbTq06dP/q86k8Xg5Gxxkdrft4uLtyeLwdQVOJPhyeJkOPFTlen0YnF2MtA1rDAdLE7eXuga77J1nH3MSm4/npXWnwvfU7WqrDzv7nGY1dsKj3+1un00tWX22eh8269wP9at6/mofGbr6kFJ9byE67G/w3XYeyvnxdWpwWIQrsNte8N9WVUnV61TVc9TWL9WndNyfcyOazB8CecaAICyumz54K756fuejFv9ZavQ/FKSSVO6QYtOM25LNLla0Rq4hdlQeuOW9L9Ki1Ek+67ZdjfSyViio6Y09TW9Wn5a6ec77b3PTnqRyPQwLlod7VhTrRFXs2y61iyRc2lLXLRkWj1KZTyxz0TPV2Mqw/f+s7L3ziNpFK2sWjZIpf1zLPt+jpNeSxrsizQOpNVIZZ634AfSW93Oq4ZrJc/UrFPnJ4lIO162gF5NdPnxzLUAR3uRTAd5i6i9N5Xox2Ct74eSxl2J9/wMAAC+AQ8LopOhDKXru6s9+5Jv7Adf3EqTQaRfx6u+6O+Yn0uv05GOfw1LQWWmIUCkm3fzP4I06fltWaDubthV/DWkMhtHcmSB6+BImkFwb77p6vTQ7Wfv4nmGUgvTFsaMdV/3PsfSPdT5FvTWCIOgdYd3PuxLN9ba5MJ5JMfvRloDs8/GyvpBd3ce7u6c00jrZ/jDyAXYVK6LXbFu9Kz+9ZKo9KOqdp1aufUIsxBsXew2dONUf4DN9d+Dzop+6svoVPx6bVhHeZjA8KYt3fDfEQAA34AHBNFUzpNlq92jsXGao5GM/Cscf5cm58vw8Ehs/Ga2rb60xr3dBT3X8pcH+KYcHYathTaecLmPdwP6c2FjYLOxvl86BjIPr6PSj5BsffZjyNWVV/Yjxrc6Wrir/kjKNY41zNqY3Sxsdq72pd0IW8Pzz1tfLjxusM6cjT11Y6D7chy8zYVo+xHl1hnJue23O4fWwvq4P64AAHguHhBEsws82jfDcniz1ibfClQIW4sewF30YwHi7LK8/kcRyUErb2n787mu+KA12C54CbvnM1kL4Oi0KdMPX+Mz+HqsNTRNhnL9euSDXCrzG50fdE+v4sonw1J4LVpX5zMZ24Vdfn7zjdbHhgV4Dagf9LPzrcjW2m0Xzk0H+revO66FMg+bbxpyXerSDxzEuk5rzb9nna7VX0Nl2Cpb9A5Ya7dd2OTnH2iYjfXden7dkAVbzp93d6Gb20ZPLjfpQQAA4Bl74BhRC0ZdiZLesoXOjbebSlKEU/0C/+VcJI6zL+EHsgDhWr7ycYGPJgsLeffxn8vGE1qw98HIvaw7fvW42vu6s5+i6LCltaWZDT0wrjs8mDbWmmhhLPxhY8MUdMnWoT8vbgxyMG2BtghsqQZK+78P7MVnaQFVg+qp/n2n5dFaVa27vaZ+FuNO71untWLrllsHfv1ZcF1Ol89b/oOnFIj1ZQHVXW1vLaoP/OEGAMBT96i3b5LS7Y2CW9WEt6PJb1GTTWXycgsiX3L7JgluC6XK78mUbqfjhesMy5a3Z9It+NvzhMq3bwpvxaP8rZ9k7XIZt27rZs4/Eztua0krhaTsGMetf0hr/D+u9a0Q3g7rOSmd3xW3L/Ll1c+rXGcqt32q1Jk7yzrZZ2mtsdlQD19//GdavgVT5dzWftbVdZryesv7stl679QNAABegLpsuWUQBQAAADZTly0ffPsmAAAAYBsEUQAAAOwEQRQAAAA7QRAFAADAThBEAQAAsBMEUQAAAOwEQRQAAAA7QRAFAADAThBEAQAAsBMEUQAAAOzEyiD63Xffye+//+6nAAAAgO1YprRsucrKIPr9999LmqaEUQAAAGzNsqRlSsuWq/xlofzfJb/++qv89ttv8scff/g5AAAAwOasJdRC6A8//ODnlNUGUQAAAOBr4mIlAAAA7ARBFAAAADtBEAUAAMBOEEQBAACwAyL/D6F4Ttdh/BOaAAAAAElFTkSuQmCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "PepXKoHrjRQw",
    "outputId": "7231d2c5-2f31-43fe-8ec0-ff73e1f1383b",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'NOMOR_KONTRAK'</li><li>'DOMISILI'</li><li>'KARAKTER'</li><li>'PROFESI'</li><li>'KONDISI_USAHA'</li><li>'KONDISI_JAMINAN'</li><li>'STATUS'</li><li>'PRODUK'</li><li>'PYD'</li><li>'TENOR'</li><li>'OSL'</li><li>'KEWAJIBAN'</li><li>'KOLEKTIBILITAS'</li><li>'COUNT_SURVEY'</li><li>'REKOMENDASI_TINDAK_LANJUT'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'NOMOR\\_KONTRAK'\n",
       "\\item 'DOMISILI'\n",
       "\\item 'KARAKTER'\n",
       "\\item 'PROFESI'\n",
       "\\item 'KONDISI\\_USAHA'\n",
       "\\item 'KONDISI\\_JAMINAN'\n",
       "\\item 'STATUS'\n",
       "\\item 'PRODUK'\n",
       "\\item 'PYD'\n",
       "\\item 'TENOR'\n",
       "\\item 'OSL'\n",
       "\\item 'KEWAJIBAN'\n",
       "\\item 'KOLEKTIBILITAS'\n",
       "\\item 'COUNT\\_SURVEY'\n",
       "\\item 'REKOMENDASI\\_TINDAK\\_LANJUT'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'NOMOR_KONTRAK'\n",
       "2. 'DOMISILI'\n",
       "3. 'KARAKTER'\n",
       "4. 'PROFESI'\n",
       "5. 'KONDISI_USAHA'\n",
       "6. 'KONDISI_JAMINAN'\n",
       "7. 'STATUS'\n",
       "8. 'PRODUK'\n",
       "9. 'PYD'\n",
       "10. 'TENOR'\n",
       "11. 'OSL'\n",
       "12. 'KEWAJIBAN'\n",
       "13. 'KOLEKTIBILITAS'\n",
       "14. 'COUNT_SURVEY'\n",
       "15. 'REKOMENDASI_TINDAK_LANJUT'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"NOMOR_KONTRAK\"             \"DOMISILI\"                 \n",
       " [3] \"KARAKTER\"                  \"PROFESI\"                  \n",
       " [5] \"KONDISI_USAHA\"             \"KONDISI_JAMINAN\"          \n",
       " [7] \"STATUS\"                    \"PRODUK\"                   \n",
       " [9] \"PYD\"                       \"TENOR\"                    \n",
       "[11] \"OSL\"                       \"KEWAJIBAN\"                \n",
       "[13] \"KOLEKTIBILITAS\"            \"COUNT_SURVEY\"             \n",
       "[15] \"REKOMENDASI_TINDAK_LANJUT\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choosing independent Variable\n",
    "colnames(data_reduce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "LLJPJQTRjpSw",
    "outputId": "a1f97639-090e-4c9b-ac1d-3369064835cc",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>KARAKTER</th><th scope=col>KONDISI_USAHA</th><th scope=col>KONDISI_JAMINAN</th><th scope=col>STATUS</th><th scope=col>KEWAJIBAN</th><th scope=col>OSL</th><th scope=col>KOLEKTIBILITAS</th><th scope=col>REKOMENDASI_TINDAK_LANJUT</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>KOOPERATIF      </td><td>2</td><td>Baik </td><td>2</td><td>4896841</td><td>28750000</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>TIDAK KOOPERATIF</td><td>3</td><td>Rusak</td><td>8</td><td>      0</td><td> 2040693</td><td>MACET                  </td><td>Penarikan      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>TIDAK KOOPERATIF</td><td>3</td><td>Baik </td><td>8</td><td>      0</td><td>       0</td><td>MACET                  </td><td>Penarikan      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>KOOPERATIF      </td><td>1</td><td>Rusak</td><td>7</td><td>      0</td><td>       0</td><td>MACET                  </td><td>Angsuran Biasa </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>TIDAK KOOPERATIF</td><td>1</td><td>Baik </td><td>8</td><td>      0</td><td>19844807</td><td>MACET                  </td><td>Penarikan      </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>KOOPERATIF      </td><td>1</td><td>Baik </td><td>7</td><td>2208516</td><td>27298726</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & KARAKTER & KONDISI\\_USAHA & KONDISI\\_JAMINAN & STATUS & KEWAJIBAN & OSL & KOLEKTIBILITAS & REKOMENDASI\\_TINDAK\\_LANJUT\\\\\n",
       "  & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & KOOPERATIF       & 2 & Baik  & 2 & 4896841 & 28750000 & DALAM PENGAWASAN KHUSUS & Angsuran Biasa \\\\\n",
       "\t2 & TIDAK KOOPERATIF & 3 & Rusak & 8 &       0 &  2040693 & MACET                   & Penarikan      \\\\\n",
       "\t3 & TIDAK KOOPERATIF & 3 & Baik  & 8 &       0 &        0 & MACET                   & Penarikan      \\\\\n",
       "\t4 & KOOPERATIF       & 1 & Rusak & 7 &       0 &        0 & MACET                   & Angsuran Biasa \\\\\n",
       "\t5 & TIDAK KOOPERATIF & 1 & Baik  & 8 &       0 & 19844807 & MACET                   & Penarikan      \\\\\n",
       "\t6 & KOOPERATIF       & 1 & Baik  & 7 & 2208516 & 27298726 & DALAM PENGAWASAN KHUSUS & Restrukturisasi\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | KARAKTER &lt;chr&gt; | KONDISI_USAHA &lt;int&gt; | KONDISI_JAMINAN &lt;chr&gt; | STATUS &lt;int&gt; | KEWAJIBAN &lt;int&gt; | OSL &lt;int&gt; | KOLEKTIBILITAS &lt;chr&gt; | REKOMENDASI_TINDAK_LANJUT &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | KOOPERATIF       | 2 | Baik  | 2 | 4896841 | 28750000 | DALAM PENGAWASAN KHUSUS | Angsuran Biasa  |\n",
       "| 2 | TIDAK KOOPERATIF | 3 | Rusak | 8 |       0 |  2040693 | MACET                   | Penarikan       |\n",
       "| 3 | TIDAK KOOPERATIF | 3 | Baik  | 8 |       0 |        0 | MACET                   | Penarikan       |\n",
       "| 4 | KOOPERATIF       | 1 | Rusak | 7 |       0 |        0 | MACET                   | Angsuran Biasa  |\n",
       "| 5 | TIDAK KOOPERATIF | 1 | Baik  | 8 |       0 | 19844807 | MACET                   | Penarikan       |\n",
       "| 6 | KOOPERATIF       | 1 | Baik  | 7 | 2208516 | 27298726 | DALAM PENGAWASAN KHUSUS | Restrukturisasi |\n",
       "\n"
      ],
      "text/plain": [
       "  KARAKTER         KONDISI_USAHA KONDISI_JAMINAN STATUS KEWAJIBAN OSL     \n",
       "1 KOOPERATIF       2             Baik            2      4896841   28750000\n",
       "2 TIDAK KOOPERATIF 3             Rusak           8            0    2040693\n",
       "3 TIDAK KOOPERATIF 3             Baik            8            0          0\n",
       "4 KOOPERATIF       1             Rusak           7            0          0\n",
       "5 TIDAK KOOPERATIF 1             Baik            8            0   19844807\n",
       "6 KOOPERATIF       1             Baik            7      2208516   27298726\n",
       "  KOLEKTIBILITAS          REKOMENDASI_TINDAK_LANJUT\n",
       "1 DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "2 MACET                   Penarikan                \n",
       "3 MACET                   Penarikan                \n",
       "4 MACET                   Angsuran Biasa           \n",
       "5 MACET                   Penarikan                \n",
       "6 DALAM PENGAWASAN KHUSUS Restrukturisasi          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select column\n",
    "data_select = data_reduce[, c(\"KARAKTER\",\"KONDISI_USAHA\",\"KONDISI_JAMINAN\",\"STATUS\",\"KEWAJIBAN\",\"OSL\",\"KOLEKTIBILITAS\",\"REKOMENDASI_TINDAK_LANJUT\")]\n",
    "\n",
    "# Delete column that have NA values\n",
    "data_non_na = na.omit(data_select)\n",
    "head(data_non_na, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c5l4h8VUkPqb"
   },
   "source": [
    "### Transformasi data\n",
    "Untuk memberikan performa model yang baik, maka pada data kita perlu dilakukan ***treatment*** tertentu, misalnya dilakukan ***scalling*** atau dilakukan pengelompokan data atau disebut juga ***bucketing***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lwZv0b9skPUB",
    "outputId": "1552d89d-8e78-4d14-e2ec-c274a54cd368",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 457 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>KARAKTER</th><th scope=col>KONDISI_USAHA</th><th scope=col>KONDISI_JAMINAN</th><th scope=col>STATUS</th><th scope=col>KEWAJIBAN</th><th scope=col>OSL</th><th scope=col>KOLEKTIBILITAS</th><th scope=col>REKOMENDASI_TINDAK_LANJUT</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>TIDAK KOOPERATIF</td><td>1</td><td>Baik         </td><td>8</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>MACET                  </td><td>Penarikan       </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>7</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>9</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>TIDAK KOOPERATIF</td><td>1</td><td>Baik         </td><td>3</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>18</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Diskon Pelunasan</td></tr>\n",
       "\t<tr><th scope=row>19</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>21</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>22</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>23</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>24</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>28</th><td>KOOPERATIF      </td><td>3</td><td>Pindah Tangan</td><td>8</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DIRAGUKAN              </td><td>Penarikan       </td></tr>\n",
       "\t<tr><th scope=row>29</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>30</th><td>TIDAK KOOPERATIF</td><td>3</td><td>Pindah Tangan</td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>32</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>34</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>35</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>39</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>3</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>41</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>42</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>43</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>46</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>47</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>48</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>50</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>51</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>952</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>953</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>954</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>LANCAR                 </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>956</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>958</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>959</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>962</th><td>TIDAK KOOPERATIF</td><td>3</td><td>Pindah Tangan</td><td>8</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Penarikan       </td></tr>\n",
       "\t<tr><th scope=row>967</th><td>TIDAK KOOPERATIF</td><td>3</td><td>Baik         </td><td>8</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Penarikan       </td></tr>\n",
       "\t<tr><th scope=row>969</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>970</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(5,15]    </td><td>DALAM PENGAWASAN KHUSUS</td><td>Diskon Pelunasan</td></tr>\n",
       "\t<tr><th scope=row>972</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>3</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>973</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>974</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>976</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>978</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>979</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>981</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>982</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>985</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>988</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>989</th><td>TIDAK KOOPERATIF</td><td>3</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>990</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>991</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>992</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>3</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>993</th><td>KOOPERATIF      </td><td>3</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>994</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Diskon Pelunasan</td></tr>\n",
       "\t<tr><th scope=row>996</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>997</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>KURANG LANCAR          </td><td>Restrukturisasi </td></tr>\n",
       "\t<tr><th scope=row>998</th><td>KOOPERATIF      </td><td>1</td><td>Baik         </td><td>2</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DIRAGUKAN              </td><td>Angsuran Biasa  </td></tr>\n",
       "\t<tr><th scope=row>1000</th><td>KOOPERATIF      </td><td>2</td><td>Baik         </td><td>7</td><td>(-0.354,5]</td><td>(-0.354,5]</td><td>DALAM PENGAWASAN KHUSUS</td><td>Angsuran Biasa  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 457 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & KARAKTER & KONDISI\\_USAHA & KONDISI\\_JAMINAN & STATUS & KEWAJIBAN & OSL & KOLEKTIBILITAS & REKOMENDASI\\_TINDAK\\_LANJUT\\\\\n",
       "  & <chr> & <int> & <chr> & <int> & <fct> & <fct> & <chr> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & KOOPERATIF       & 2 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t5 & TIDAK KOOPERATIF & 1 & Baik          & 8 & (-0.354,5{]} & (-0.354,5{]} & MACET                   & Penarikan       \\\\\n",
       "\t6 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t7 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t9 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Restrukturisasi \\\\\n",
       "\t11 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t15 & TIDAK KOOPERATIF & 1 & Baik          & 3 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t18 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Diskon Pelunasan\\\\\n",
       "\t19 & KOOPERATIF       & 3 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t21 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t22 & KOOPERATIF       & 2 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t23 & KOOPERATIF       & 2 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Angsuran Biasa  \\\\\n",
       "\t24 & KOOPERATIF       & 2 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Restrukturisasi \\\\\n",
       "\t28 & KOOPERATIF       & 3 & Pindah Tangan & 8 & (-0.354,5{]} & (-0.354,5{]} & DIRAGUKAN               & Penarikan       \\\\\n",
       "\t29 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Angsuran Biasa  \\\\\n",
       "\t30 & TIDAK KOOPERATIF & 3 & Pindah Tangan & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t32 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Restrukturisasi \\\\\n",
       "\t33 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t34 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t35 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t39 & KOOPERATIF       & 2 & Baik          & 3 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t40 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t41 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t42 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t43 & KOOPERATIF       & 1 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t46 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Restrukturisasi \\\\\n",
       "\t47 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Restrukturisasi \\\\\n",
       "\t48 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t50 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Angsuran Biasa  \\\\\n",
       "\t51 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t952 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t953 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t954 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & LANCAR                  & Restrukturisasi \\\\\n",
       "\t956 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t958 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t959 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t962 & TIDAK KOOPERATIF & 3 & Pindah Tangan & 8 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Penarikan       \\\\\n",
       "\t967 & TIDAK KOOPERATIF & 3 & Baik          & 8 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Penarikan       \\\\\n",
       "\t969 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t970 & KOOPERATIF       & 1 & Baik          & 7 & (-0.354,5{]} & (5,15{]}     & DALAM PENGAWASAN KHUSUS & Diskon Pelunasan\\\\\n",
       "\t972 & KOOPERATIF       & 3 & Baik          & 3 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t973 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t974 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t976 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Restrukturisasi \\\\\n",
       "\t978 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t979 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t981 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t982 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t985 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t988 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t989 & TIDAK KOOPERATIF & 3 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t990 & KOOPERATIF       & 3 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t991 & KOOPERATIF       & 3 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t992 & KOOPERATIF       & 2 & Baik          & 3 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\t993 & KOOPERATIF       & 3 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Restrukturisasi \\\\\n",
       "\t994 & KOOPERATIF       & 1 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Diskon Pelunasan\\\\\n",
       "\t996 & KOOPERATIF       & 1 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Restrukturisasi \\\\\n",
       "\t997 & KOOPERATIF       & 1 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & KURANG LANCAR           & Restrukturisasi \\\\\n",
       "\t998 & KOOPERATIF       & 1 & Baik          & 2 & (-0.354,5{]} & (-0.354,5{]} & DIRAGUKAN               & Angsuran Biasa  \\\\\n",
       "\t1000 & KOOPERATIF       & 2 & Baik          & 7 & (-0.354,5{]} & (-0.354,5{]} & DALAM PENGAWASAN KHUSUS & Angsuran Biasa  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 457 × 8\n",
       "\n",
       "| <!--/--> | KARAKTER &lt;chr&gt; | KONDISI_USAHA &lt;int&gt; | KONDISI_JAMINAN &lt;chr&gt; | STATUS &lt;int&gt; | KEWAJIBAN &lt;fct&gt; | OSL &lt;fct&gt; | KOLEKTIBILITAS &lt;chr&gt; | REKOMENDASI_TINDAK_LANJUT &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | KOOPERATIF       | 2 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 5 | TIDAK KOOPERATIF | 1 | Baik          | 8 | (-0.354,5] | (-0.354,5] | MACET                   | Penarikan        |\n",
       "| 6 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 7 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 9 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Restrukturisasi  |\n",
       "| 11 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 15 | TIDAK KOOPERATIF | 1 | Baik          | 3 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 18 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Diskon Pelunasan |\n",
       "| 19 | KOOPERATIF       | 3 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 21 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 22 | KOOPERATIF       | 2 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 23 | KOOPERATIF       | 2 | Baik          | 2 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Angsuran Biasa   |\n",
       "| 24 | KOOPERATIF       | 2 | Baik          | 2 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Restrukturisasi  |\n",
       "| 28 | KOOPERATIF       | 3 | Pindah Tangan | 8 | (-0.354,5] | (-0.354,5] | DIRAGUKAN               | Penarikan        |\n",
       "| 29 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Angsuran Biasa   |\n",
       "| 30 | TIDAK KOOPERATIF | 3 | Pindah Tangan | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 32 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Restrukturisasi  |\n",
       "| 33 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 34 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 35 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 39 | KOOPERATIF       | 2 | Baik          | 3 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 40 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 41 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 42 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 43 | KOOPERATIF       | 1 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 46 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Restrukturisasi  |\n",
       "| 47 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Restrukturisasi  |\n",
       "| 48 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 50 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Angsuran Biasa   |\n",
       "| 51 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 952 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 953 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 954 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | LANCAR                  | Restrukturisasi  |\n",
       "| 956 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 958 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 959 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 962 | TIDAK KOOPERATIF | 3 | Pindah Tangan | 8 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Penarikan        |\n",
       "| 967 | TIDAK KOOPERATIF | 3 | Baik          | 8 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Penarikan        |\n",
       "| 969 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 970 | KOOPERATIF       | 1 | Baik          | 7 | (-0.354,5] | (5,15]     | DALAM PENGAWASAN KHUSUS | Diskon Pelunasan |\n",
       "| 972 | KOOPERATIF       | 3 | Baik          | 3 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 973 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 974 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 976 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Restrukturisasi  |\n",
       "| 978 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 979 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 981 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 982 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 985 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 988 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 989 | TIDAK KOOPERATIF | 3 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 990 | KOOPERATIF       | 3 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 991 | KOOPERATIF       | 3 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 992 | KOOPERATIF       | 2 | Baik          | 3 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "| 993 | KOOPERATIF       | 3 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Restrukturisasi  |\n",
       "| 994 | KOOPERATIF       | 1 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Diskon Pelunasan |\n",
       "| 996 | KOOPERATIF       | 1 | Baik          | 2 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Restrukturisasi  |\n",
       "| 997 | KOOPERATIF       | 1 | Baik          | 2 | (-0.354,5] | (-0.354,5] | KURANG LANCAR           | Restrukturisasi  |\n",
       "| 998 | KOOPERATIF       | 1 | Baik          | 2 | (-0.354,5] | (-0.354,5] | DIRAGUKAN               | Angsuran Biasa   |\n",
       "| 1000 | KOOPERATIF       | 2 | Baik          | 7 | (-0.354,5] | (-0.354,5] | DALAM PENGAWASAN KHUSUS | Angsuran Biasa   |\n",
       "\n"
      ],
      "text/plain": [
       "     KARAKTER         KONDISI_USAHA KONDISI_JAMINAN STATUS KEWAJIBAN \n",
       "1    KOOPERATIF       2             Baik            2      (-0.354,5]\n",
       "5    TIDAK KOOPERATIF 1             Baik            8      (-0.354,5]\n",
       "6    KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "7    KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "9    KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "11   KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "15   TIDAK KOOPERATIF 1             Baik            3      (-0.354,5]\n",
       "18   KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "19   KOOPERATIF       3             Baik            2      (-0.354,5]\n",
       "21   KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "22   KOOPERATIF       2             Baik            2      (-0.354,5]\n",
       "23   KOOPERATIF       2             Baik            2      (-0.354,5]\n",
       "24   KOOPERATIF       2             Baik            2      (-0.354,5]\n",
       "28   KOOPERATIF       3             Pindah Tangan   8      (-0.354,5]\n",
       "29   KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "30   TIDAK KOOPERATIF 3             Pindah Tangan   7      (-0.354,5]\n",
       "32   KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "33   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "34   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "35   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "39   KOOPERATIF       2             Baik            3      (-0.354,5]\n",
       "40   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "41   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "42   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "43   KOOPERATIF       1             Baik            2      (-0.354,5]\n",
       "46   KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "47   KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "48   KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "50   KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "51   KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "⋮    ⋮                ⋮             ⋮               ⋮      ⋮         \n",
       "952  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "953  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "954  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "956  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "958  KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "959  KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "962  TIDAK KOOPERATIF 3             Pindah Tangan   8      (-0.354,5]\n",
       "967  TIDAK KOOPERATIF 3             Baik            8      (-0.354,5]\n",
       "969  KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "970  KOOPERATIF       1             Baik            7      (-0.354,5]\n",
       "972  KOOPERATIF       3             Baik            3      (-0.354,5]\n",
       "973  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "974  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "976  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "978  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "979  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "981  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "982  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "985  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "988  KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "989  TIDAK KOOPERATIF 3             Baik            2      (-0.354,5]\n",
       "990  KOOPERATIF       3             Baik            2      (-0.354,5]\n",
       "991  KOOPERATIF       3             Baik            7      (-0.354,5]\n",
       "992  KOOPERATIF       2             Baik            3      (-0.354,5]\n",
       "993  KOOPERATIF       3             Baik            2      (-0.354,5]\n",
       "994  KOOPERATIF       1             Baik            2      (-0.354,5]\n",
       "996  KOOPERATIF       1             Baik            2      (-0.354,5]\n",
       "997  KOOPERATIF       1             Baik            2      (-0.354,5]\n",
       "998  KOOPERATIF       1             Baik            2      (-0.354,5]\n",
       "1000 KOOPERATIF       2             Baik            7      (-0.354,5]\n",
       "     OSL        KOLEKTIBILITAS          REKOMENDASI_TINDAK_LANJUT\n",
       "1    (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "5    (-0.354,5] MACET                   Penarikan                \n",
       "6    (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "7    (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "9    (-0.354,5] LANCAR                  Restrukturisasi          \n",
       "11   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "15   (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "18   (-0.354,5] DALAM PENGAWASAN KHUSUS Diskon Pelunasan         \n",
       "19   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "21   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "22   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "23   (-0.354,5] KURANG LANCAR           Angsuran Biasa           \n",
       "24   (-0.354,5] KURANG LANCAR           Restrukturisasi          \n",
       "28   (-0.354,5] DIRAGUKAN               Penarikan                \n",
       "29   (-0.354,5] KURANG LANCAR           Angsuran Biasa           \n",
       "30   (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "32   (-0.354,5] LANCAR                  Restrukturisasi          \n",
       "33   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "34   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "35   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "39   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "40   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "41   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "42   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "43   (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "46   (-0.354,5] KURANG LANCAR           Restrukturisasi          \n",
       "47   (-0.354,5] LANCAR                  Restrukturisasi          \n",
       "48   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "50   (-0.354,5] LANCAR                  Angsuran Biasa           \n",
       "51   (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "⋮    ⋮          ⋮                       ⋮                        \n",
       "952  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "953  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "954  (-0.354,5] LANCAR                  Restrukturisasi          \n",
       "956  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "958  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "959  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "962  (-0.354,5] KURANG LANCAR           Penarikan                \n",
       "967  (-0.354,5] DALAM PENGAWASAN KHUSUS Penarikan                \n",
       "969  (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "970  (5,15]     DALAM PENGAWASAN KHUSUS Diskon Pelunasan         \n",
       "972  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "973  (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "974  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "976  (-0.354,5] KURANG LANCAR           Restrukturisasi          \n",
       "978  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "979  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "981  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "982  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "985  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "988  (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "989  (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "990  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "991  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "992  (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           \n",
       "993  (-0.354,5] DALAM PENGAWASAN KHUSUS Restrukturisasi          \n",
       "994  (-0.354,5] DALAM PENGAWASAN KHUSUS Diskon Pelunasan         \n",
       "996  (-0.354,5] KURANG LANCAR           Restrukturisasi          \n",
       "997  (-0.354,5] KURANG LANCAR           Restrukturisasi          \n",
       "998  (-0.354,5] DIRAGUKAN               Angsuran Biasa           \n",
       "1000 (-0.354,5] DALAM PENGAWASAN KHUSUS Angsuran Biasa           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_select_new = data_select\n",
    "data_select_new$KEWAJIBAN = scale(data_select_new$KEWAJIBAN) [, 1]\n",
    "data_select_new$OSL = scale(data_select_new$OSL) [, 1]\n",
    "data_select_new$KEWAJIBAN = cut(data_select_new$KEWAJIBAN, breaks = c(-0.354107,5,15,30))\n",
    "data_select_new$KEWAJIBAN = as.factor(data_select_new$KEWAJIBAN)\n",
    "data_select_new$OSL = cut(data_select_new$OSL, breaks = c(-0.354107,5,15,30))\n",
    "data_select_new$OSL = as.factor(data_select_new$OSL)\n",
    "data_select_new = na.omit(data_select_new)\n",
    "data_select_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mlKbN8FymLyy"
   },
   "source": [
    "### Training data\n",
    "Sebelum masuk pada pemodelan, kita perlu memisahkan data kita menjadi training dan testing (ada pula yang membaginya menjadi training, testing, dan validasi).\n",
    "\n",
    "Tujuan dari pemisahan data ini ialah untuk melihat kemampuan model kita untuk melakukan prediksi sebagaimana tujuan dari pemodelan kita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zLmpxry7mNrs",
    "outputId": "0ce9c78d-4adc-4384-c29c-9da74fe78f37",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "also installing the dependencies ‘listenv’, ‘parallelly’, ‘future’, ‘globals’, ‘shape’, ‘future.apply’, ‘numDeriv’, ‘progressr’, ‘SQUAREM’, ‘diagram’, ‘lava’, ‘prodlim’, ‘proxy’, ‘iterators’, ‘clock’, ‘gower’, ‘hardhat’, ‘ipred’, ‘timeDate’, ‘e1071’, ‘foreach’, ‘ModelMetrics’, ‘pROC’, ‘recipes’\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages('caret')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sGnswC53oGZv",
    "outputId": "b80f3391-ccbc-4764-e4ee-ff2a5e099639",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages('lattice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "id": "SEKXt4CHmVnf",
    "outputId": "45779133-89b3-45cf-c8f6-0972f3f91b68",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: lattice\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>437</li><li>8</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 437\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 437\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 437   8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(caret)\n",
    "\n",
    "# Create an index to split dataset with a proportion of 95% training data and 5% test data\n",
    "index = createDataPartition(data_select_new$REKOMENDASI_TINDAK_LANJUT, p= .95, list = FALSE)\n",
    "\n",
    "# Selecting rows that match the index for the training data.\n",
    "train = data_select_new[index, ]\n",
    "\n",
    "# Selecting rows that do not match the index for the test data.\n",
    "test = data_select_new[-index, ]\n",
    "\n",
    "# Display the dimensions of the training dataset\n",
    "dim(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "SYqT6Zu6mq4h",
    "outputId": "5a9a7e09-eacc-43b7-ee26-e989af8d4722"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>20</li><li>8</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20  8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the dimensions of the training dataset\n",
    "dim(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4fOq5N74myni"
   },
   "source": [
    "### Pemodelan/Modelling data\n",
    "Sekarang kita siap untuk masuk pada pemodelan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZyYd4qy6myWX",
    "outputId": "16b0ee3b-7644-44ca-d133-4514516f22d9",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: nnet\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train2 = train\n",
    "\n",
    "# Setting the reference\n",
    "train2$REKOMENDASI_TINDAK_LANJUT = relevel(train$REKOMENDASI_TINDAK_LANJUT, ref = \"Angsuran Biasa\")\n",
    "\n",
    "# Training model\n",
    "require(nnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q3jagQv0pI6Q",
    "outputId": "87b9282f-b421-40df-ba3f-f8acdb15d095",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# weights:  64 (45 variable)\n",
      "initial  value 605.810636 \n",
      "iter  10 value 297.757924\n",
      "iter  20 value 264.894804\n",
      "iter  30 value 263.870970\n",
      "iter  40 value 263.799881\n",
      "final  value 263.799477 \n",
      "converged\n"
     ]
    }
   ],
   "source": [
    "# Training the multinomial model\n",
    "multinom_model = multinom(REKOMENDASI_TINDAK_LANJUT ~ ., data=train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "cOWrs3QGpg-g",
    "outputId": "02ff35b0-e236-458d-f901-5dda351512b1",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in sqrt(diag(vc)):\n",
      "“NaNs produced”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "multinom(formula = REKOMENDASI_TINDAK_LANJUT ~ ., data = train2)\n",
       "\n",
       "Coefficients:\n",
       "                 (Intercept) KARAKTERTIDAK KOOPERATIF KONDISI_USAHA\n",
       "Diskon Pelunasan   11.244759                -14.62624    -13.852918\n",
       "Penarikan         -57.307161                 48.65765      1.623541\n",
       "Restrukturisasi    -3.537153                -56.28708      1.068860\n",
       "                 KONDISI_JAMINANHilang KONDISI_JAMINANPindah Tangan\n",
       "Diskon Pelunasan             -1.526697                    0.6409944\n",
       "Penarikan                   175.539677                   -0.9872966\n",
       "Restrukturisasi             -27.468739                  -48.5727355\n",
       "                 KONDISI_JAMINANRusak     STATUS KEWAJIBAN(5,15]\n",
       "Diskon Pelunasan             1.360318 0.04943448               0\n",
       "Penarikan                   10.588672 0.45204514               0\n",
       "Restrukturisasi             -1.143673 0.26073683               0\n",
       "                 KEWAJIBAN(15,30]  OSL(5,15] OSL(15,30] KOLEKTIBILITASDIRAGUKAN\n",
       "Diskon Pelunasan        -2.170965  3.5123626          0               2.1792674\n",
       "Penarikan                5.229486 11.4382856          0              51.1105568\n",
       "Restrukturisasi         32.741337  0.2801508          0               0.3589651\n",
       "                 KOLEKTIBILITASKURANG LANCAR KOLEKTIBILITASLANCAR\n",
       "Diskon Pelunasan                 -15.3452007           0.04231237\n",
       "Penarikan                         48.2276143         -13.59573510\n",
       "Restrukturisasi                    0.2282938           0.28915776\n",
       "                 KOLEKTIBILITASMACET\n",
       "Diskon Pelunasan           0.8004984\n",
       "Penarikan                 29.4856261\n",
       "Restrukturisasi          -42.5278736\n",
       "\n",
       "Std. Errors:\n",
       "                 (Intercept) KARAKTERTIDAK KOOPERATIF KONDISI_USAHA\n",
       "Diskon Pelunasan   0.5895200             2.762700e-08     0.5895225\n",
       "Penarikan          2.4908851             1.253091e+00     0.9338879\n",
       "Restrukturisasi    0.4927166             1.983197e-24     0.1478515\n",
       "                 KONDISI_JAMINANHilang KONDISI_JAMINANPindah Tangan\n",
       "Diskon Pelunasan                   NaN                 2.005035e-12\n",
       "Penarikan                 4.815124e-57                 1.849126e+00\n",
       "Restrukturisasi           1.913204e-65                 7.626743e-21\n",
       "                 KONDISI_JAMINANRusak     STATUS KEWAJIBAN(5,15]\n",
       "Diskon Pelunasan                  NaN 0.20452751             NaN\n",
       "Penarikan                8.143066e-18 0.30009668               0\n",
       "Restrukturisasi          1.457280e-60 0.05600754               0\n",
       "                 KEWAJIBAN(15,30]    OSL(5,15]   OSL(15,30]\n",
       "Diskon Pelunasan     7.565950e-16 1.459643e+00 9.434972e-16\n",
       "Penarikan            1.953113e-28 1.445723e-16 0.000000e+00\n",
       "Restrukturisasi      8.107070e-15 1.283024e+00 0.000000e+00\n",
       "                 KOLEKTIBILITASDIRAGUKAN KOLEKTIBILITASKURANG LANCAR\n",
       "Diskon Pelunasan                1.396502                1.204666e-07\n",
       "Penarikan                       1.148255                9.914857e-01\n",
       "Restrukturisasi                 1.064189                3.089658e-01\n",
       "                 KOLEKTIBILITASLANCAR KOLEKTIBILITASMACET\n",
       "Diskon Pelunasan         9.855036e-01        3.085910e-13\n",
       "Penarikan                5.662788e-07        1.103508e-09\n",
       "Restrukturisasi          2.896531e-01        2.005951e-20\n",
       "\n",
       "Residual Deviance: 527.599 \n",
       "AIC: 605.599 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking model\n",
    "summary(multinom_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 216
    },
    "id": "hy4fhoe4plvz",
    "outputId": "33bc9be7-fed0-4859-8475-caf598c57995",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 15 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>(Intercept)</th><th scope=col>KARAKTERTIDAK KOOPERATIF</th><th scope=col>KONDISI_USAHA</th><th scope=col>KONDISI_JAMINANHilang</th><th scope=col>KONDISI_JAMINANPindah Tangan</th><th scope=col>KONDISI_JAMINANRusak</th><th scope=col>STATUS</th><th scope=col>KEWAJIBAN(5,15]</th><th scope=col>KEWAJIBAN(15,30]</th><th scope=col>OSL(5,15]</th><th scope=col>OSL(15,30]</th><th scope=col>KOLEKTIBILITASDIRAGUKAN</th><th scope=col>KOLEKTIBILITASKURANG LANCAR</th><th scope=col>KOLEKTIBILITASLANCAR</th><th scope=col>KOLEKTIBILITASMACET</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Diskon Pelunasan</th><td>7.647808e+04</td><td>4.445317e-07</td><td>9.632836e-07</td><td>2.172521e-01</td><td>1.898368e+00</td><td>3.897433e+00</td><td>1.050677</td><td>1</td><td>1.140674e-01</td><td>   33.527387</td><td>1</td><td>8.839828e+00</td><td>2.166028e-07</td><td>1.043220e+00</td><td>2.226650e+00</td></tr>\n",
       "\t<tr><th scope=row>Penarikan</th><td>1.293649e-25</td><td>1.354404e+21</td><td>5.071016e+00</td><td>1.721523e+76</td><td>3.725826e-01</td><td>3.968275e+04</td><td>1.571523</td><td>1</td><td>1.866968e+02</td><td>92807.767215</td><td>1</td><td>1.574102e+22</td><td>8.810220e+20</td><td>1.245797e-06</td><td>6.389174e+12</td></tr>\n",
       "\t<tr><th scope=row>Restrukturisasi</th><td>2.909605e-02</td><td>3.587822e-25</td><td>2.912057e+00</td><td>1.176192e-12</td><td>8.037648e-22</td><td>3.186465e-01</td><td>1.297886</td><td>1</td><td>1.657226e+14</td><td>    1.323329</td><td>1</td><td>1.431847e+00</td><td>1.256454e+00</td><td>1.335302e+00</td><td>3.391401e-19</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 3 × 15 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & (Intercept) & KARAKTERTIDAK KOOPERATIF & KONDISI\\_USAHA & KONDISI\\_JAMINANHilang & KONDISI\\_JAMINANPindah Tangan & KONDISI\\_JAMINANRusak & STATUS & KEWAJIBAN(5,15{]} & KEWAJIBAN(15,30{]} & OSL(5,15{]} & OSL(15,30{]} & KOLEKTIBILITASDIRAGUKAN & KOLEKTIBILITASKURANG LANCAR & KOLEKTIBILITASLANCAR & KOLEKTIBILITASMACET\\\\\n",
       "\\hline\n",
       "\tDiskon Pelunasan & 7.647808e+04 & 4.445317e-07 & 9.632836e-07 & 2.172521e-01 & 1.898368e+00 & 3.897433e+00 & 1.050677 & 1 & 1.140674e-01 &    33.527387 & 1 & 8.839828e+00 & 2.166028e-07 & 1.043220e+00 & 2.226650e+00\\\\\n",
       "\tPenarikan & 1.293649e-25 & 1.354404e+21 & 5.071016e+00 & 1.721523e+76 & 3.725826e-01 & 3.968275e+04 & 1.571523 & 1 & 1.866968e+02 & 92807.767215 & 1 & 1.574102e+22 & 8.810220e+20 & 1.245797e-06 & 6.389174e+12\\\\\n",
       "\tRestrukturisasi & 2.909605e-02 & 3.587822e-25 & 2.912057e+00 & 1.176192e-12 & 8.037648e-22 & 3.186465e-01 & 1.297886 & 1 & 1.657226e+14 &     1.323329 & 1 & 1.431847e+00 & 1.256454e+00 & 1.335302e+00 & 3.391401e-19\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 3 × 15 of type dbl\n",
       "\n",
       "| <!--/--> | (Intercept) | KARAKTERTIDAK KOOPERATIF | KONDISI_USAHA | KONDISI_JAMINANHilang | KONDISI_JAMINANPindah Tangan | KONDISI_JAMINANRusak | STATUS | KEWAJIBAN(5,15] | KEWAJIBAN(15,30] | OSL(5,15] | OSL(15,30] | KOLEKTIBILITASDIRAGUKAN | KOLEKTIBILITASKURANG LANCAR | KOLEKTIBILITASLANCAR | KOLEKTIBILITASMACET |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Diskon Pelunasan | 7.647808e+04 | 4.445317e-07 | 9.632836e-07 | 2.172521e-01 | 1.898368e+00 | 3.897433e+00 | 1.050677 | 1 | 1.140674e-01 |    33.527387 | 1 | 8.839828e+00 | 2.166028e-07 | 1.043220e+00 | 2.226650e+00 |\n",
       "| Penarikan | 1.293649e-25 | 1.354404e+21 | 5.071016e+00 | 1.721523e+76 | 3.725826e-01 | 3.968275e+04 | 1.571523 | 1 | 1.866968e+02 | 92807.767215 | 1 | 1.574102e+22 | 8.810220e+20 | 1.245797e-06 | 6.389174e+12 |\n",
       "| Restrukturisasi | 2.909605e-02 | 3.587822e-25 | 2.912057e+00 | 1.176192e-12 | 8.037648e-22 | 3.186465e-01 | 1.297886 | 1 | 1.657226e+14 |     1.323329 | 1 | 1.431847e+00 | 1.256454e+00 | 1.335302e+00 | 3.391401e-19 |\n",
       "\n"
      ],
      "text/plain": [
       "                 (Intercept)  KARAKTERTIDAK KOOPERATIF KONDISI_USAHA\n",
       "Diskon Pelunasan 7.647808e+04 4.445317e-07             9.632836e-07 \n",
       "Penarikan        1.293649e-25 1.354404e+21             5.071016e+00 \n",
       "Restrukturisasi  2.909605e-02 3.587822e-25             2.912057e+00 \n",
       "                 KONDISI_JAMINANHilang KONDISI_JAMINANPindah Tangan\n",
       "Diskon Pelunasan 2.172521e-01          1.898368e+00                \n",
       "Penarikan        1.721523e+76          3.725826e-01                \n",
       "Restrukturisasi  1.176192e-12          8.037648e-22                \n",
       "                 KONDISI_JAMINANRusak STATUS   KEWAJIBAN(5,15] KEWAJIBAN(15,30]\n",
       "Diskon Pelunasan 3.897433e+00         1.050677 1               1.140674e-01    \n",
       "Penarikan        3.968275e+04         1.571523 1               1.866968e+02    \n",
       "Restrukturisasi  3.186465e-01         1.297886 1               1.657226e+14    \n",
       "                 OSL(5,15]    OSL(15,30] KOLEKTIBILITASDIRAGUKAN\n",
       "Diskon Pelunasan    33.527387 1          8.839828e+00           \n",
       "Penarikan        92807.767215 1          1.574102e+22           \n",
       "Restrukturisasi      1.323329 1          1.431847e+00           \n",
       "                 KOLEKTIBILITASKURANG LANCAR KOLEKTIBILITASLANCAR\n",
       "Diskon Pelunasan 2.166028e-07                1.043220e+00        \n",
       "Penarikan        8.810220e+20                1.245797e-06        \n",
       "Restrukturisasi  1.256454e+00                1.335302e+00        \n",
       "                 KOLEKTIBILITASMACET\n",
       "Diskon Pelunasan 2.226650e+00       \n",
       "Penarikan        6.389174e+12       \n",
       "Restrukturisasi  3.391401e-19       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# converting the coefficients to odds by taking the exponential of the coefficients\n",
    "exp(coef(multinom_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "47QhqQLXpqP6",
    "outputId": "b8078049-65c2-4d50-ae62-3bcc42b83dd7",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Angsuran Biasa</th><th scope=col>Diskon Pelunasan</th><th scope=col>Penarikan</th><th scope=col>Restrukturisasi</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0.71</td><td>0.00</td><td>0</td><td>0.29</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>0.00</td><td>0.00</td><td>1</td><td>0.00</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>0.61</td><td>0.06</td><td>0</td><td>0.32</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>0.18</td><td>0.00</td><td>0</td><td>0.82</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>0.33</td><td>0.00</td><td>0</td><td>0.67</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>0.18</td><td>0.00</td><td>0</td><td>0.82</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Angsuran Biasa & Diskon Pelunasan & Penarikan & Restrukturisasi\\\\\n",
       "\\hline\n",
       "\t1 & 0.71 & 0.00 & 0 & 0.29\\\\\n",
       "\t5 & 0.00 & 0.00 & 1 & 0.00\\\\\n",
       "\t6 & 0.61 & 0.06 & 0 & 0.32\\\\\n",
       "\t7 & 0.18 & 0.00 & 0 & 0.82\\\\\n",
       "\t9 & 0.33 & 0.00 & 0 & 0.67\\\\\n",
       "\t11 & 0.18 & 0.00 & 0 & 0.82\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | Angsuran Biasa | Diskon Pelunasan | Penarikan | Restrukturisasi |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 0.71 | 0.00 | 0 | 0.29 |\n",
       "| 5 | 0.00 | 0.00 | 1 | 0.00 |\n",
       "| 6 | 0.61 | 0.06 | 0 | 0.32 |\n",
       "| 7 | 0.18 | 0.00 | 0 | 0.82 |\n",
       "| 9 | 0.33 | 0.00 | 0 | 0.67 |\n",
       "| 11 | 0.18 | 0.00 | 0 | 0.82 |\n",
       "\n"
      ],
      "text/plain": [
       "   Angsuran Biasa Diskon Pelunasan Penarikan Restrukturisasi\n",
       "1  0.71           0.00             0         0.29           \n",
       "5  0.00           0.00             1         0.00           \n",
       "6  0.61           0.06             0         0.32           \n",
       "7  0.18           0.00             0         0.82           \n",
       "9  0.33           0.00             0         0.67           \n",
       "11 0.18           0.00             0         0.82           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(round(fitted(multinom_model), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ukq7wZhCpvUs",
    "outputId": "e99ba64b-cca7-4760-f867-805897b09360",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in df$max = apply(df, 1, max):\n",
      "“Coercing LHS to a list”\n"
     ]
    }
   ],
   "source": [
    "# predicting the values for train dataset\n",
    "train2$ClassPredicted = predict(multinom_model, newdata = train2, \"class\")\n",
    "train_prob = predict(multinom_model, newdata = train2, \"probs\")\n",
    "df = train_prob\n",
    "df$max = apply(df,1,max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E8wJSKqKpzb6",
    "outputId": "eef88bc1-f331-4da0-9e29-4754f0a2fcf3",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in df2$max = apply(df2, 1, max):\n",
      "“Coercing LHS to a list”\n"
     ]
    }
   ],
   "source": [
    "train2$score = df$max\n",
    "test_prob = predict(multinom_model, newdata = test, \"probs\")\n",
    "df2 = test_prob\n",
    "df2$max = apply(df2,1,max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qSY7s6NwyW_h"
   },
   "source": [
    "Untuk keperluan tertentu, kita perlu mengetahui peluang dari tiap baris data (perwakilan dari pelanggan) untuk masuk pada kelas target tertentu.\n",
    "\n",
    "Tujuannya ialah untuk melihat seberapa pengaruh model untuk melakukan klasifikasi. Selain itu bisa juga dari sudut pandang bisnis, dalam kasus ***real*** penentuan ***threshold*** pada nilai peluang juga dikaitkan dengan beberapa faktor, misalnya ***revenue*** pelanggan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "amZGSWPDyeGG"
   },
   "source": [
    "Cobalah lihat nilai peluang bahwa suatu pelanggan masuk pada kelas target tertentu !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "-fVB97g6p1DV",
    "outputId": "2ade23e8-9dd0-456c-d8d6-4e8f44625c13",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "68.6499"
      ],
      "text/latex": [
       "68.6499"
      ],
      "text/markdown": [
       "68.6499"
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      "text/plain": [
       "[1] 68.6499"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building classification table\n",
    "tab_train = table(train2$REKOMENDASI_TINDAK_LANJUT, train2$ClassPredicted)\n",
    "round((sum(diag(tab_train))/sum(tab_train))*100,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "WsbArHcep3wK",
    "outputId": "5ccabf57-ed86-4726-ead0-27bb52595aae",
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "70"
      ],
      "text/latex": [
       "70"
      ],
      "text/markdown": [
       "70"
      ],
      "text/plain": [
       "[1] 70"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test$ClassPredicted = predict(multinom_model, newdata = test, \"class\")\n",
    "test$score = df2$max\n",
    "tab_test = table(test$REKOMENDASI_TINDAK_LANJUT, test$ClassPredicted)\n",
    "round((sum(diag(tab_test))/sum(tab_test))*100,4)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
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   "language": "R",
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