Proyek_Machine_Learning/Dataset/Hospital_Indonesia_Datasets.ipynb

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          "ename": "",
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          "output_type": "error",
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            "\u001b[1;31mRunning cells with '.venv (Python 3.12.0)' requires the ipykernel package.\n",
            "\u001b[1;31mInstall 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/adan/OneDrive/Desktop/Machine Learning Kelompok/.venv/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "traceback": [
            "\u001b[1;31mRunning cells with '.venv (Python 3.12.0)' requires the ipykernel package.\n",
            "\u001b[1;31mInstall 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/adan/OneDrive/Desktop/Machine Learning Kelompok/.venv/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "pd.set_option('display.max_rows', 100)\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.width', None)\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "df = pd.read_csv(\"Hospital_Indonesia_datasets.csv\", sep=';')\n",
        "\n",
        "df.head(100)\n"
      ]
    },
    {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3155 entries, 0 to 3154\n",
            "Data columns (total 12 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   id                  3155 non-null   int64 \n",
            " 1   nama                3155 non-null   object\n",
            " 2   propinsi            3155 non-null   object\n",
            " 3   kab                 3155 non-null   object\n",
            " 4   alamat              3155 non-null   object\n",
            " 5   jenis               3155 non-null   object\n",
            " 6   kelas               3155 non-null   object\n",
            " 7   status_blu          3155 non-null   object\n",
            " 8   kepemilikan         3155 non-null   object\n",
            " 9   total_tempat_tidur  3155 non-null   int64 \n",
            " 10  total_layanan       3155 non-null   int64 \n",
            " 11  total_tenaga_kerja  3155 non-null   int64 \n",
            "dtypes: int64(4), object(8)\n",
            "memory usage: 295.9+ KB\n",
            "None\n",
            "id                    0\n",
            "nama                  0\n",
            "propinsi              0\n",
            "kab                   0\n",
            "alamat                0\n",
            "jenis                 0\n",
            "kelas                 0\n",
            "status_blu            0\n",
            "kepemilikan           0\n",
            "total_tempat_tidur    0\n",
            "total_layanan         0\n",
            "total_tenaga_kerja    0\n",
            "dtype: int64\n"
          ]
        },
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              "      <td>3155.000000</td>\n",
              "      <td>3155.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.803327e+06</td>\n",
              "      <td>135.433914</td>\n",
              "      <td>41.097306</td>\n",
              "      <td>261.980666</td>\n",
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              "      <th>std</th>\n",
              "      <td>2.005303e+06</td>\n",
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              "      <td>30.583893</td>\n",
              "      <td>382.352731</td>\n",
              "    </tr>\n",
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              "      <th>min</th>\n",
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              "      <td>31.000000</td>\n",
              "      <td>153.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>5.103046e+06</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>335.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.271080e+06</td>\n",
              "      <td>30343.000000</td>\n",
              "      <td>419.000000</td>\n",
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            ],
            "text/plain": [
              "                 id  total_tempat_tidur  total_layanan  total_tenaga_kerja\n",
              "count  3.155000e+03         3155.000000    3155.000000         3155.000000\n",
              "mean   3.803327e+06          135.433914      41.097306          261.980666\n",
              "std    2.005303e+06          550.960235      30.583893          382.352731\n",
              "min    1.101015e+06            0.000000       1.000000            0.000000\n",
              "25%    3.171859e+06           54.000000      22.000000           56.500000\n",
              "50%    3.325039e+06          102.000000      31.000000          153.000000\n",
              "75%    5.103046e+06          155.000000      50.000000          335.000000\n",
              "max    9.271080e+06        30343.000000     419.000000         7939.000000"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ============================================================\n",
        "# 3. CEK MISSING VALUE & INFO DATASET\n",
        "# ============================================================\n",
        "print(df.info())\n",
        "print(df.isnull().sum())\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        },
        "id": "fhC8RfMucAb6",
        "outputId": "4b5bcc26-d18a-4095-a0e0-096daf21c2a9"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Grafik jumlah kelas\n",
        "plt.figure(figsize=(7,5))\n",
        "sns.countplot(x=df['kelas'])\n",
        "plt.title(\"Distribusi Kelas\")\n",
        "plt.show()\n",
        "\n",
        "# Heatmap korelasi numerik\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')\n",
        "plt.title(\"Heatmap Korelasi Fitur\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "XAHzDeJqcCZq",
        "outputId": "7bc3ae3d-45b5-436a-e45a-74a693e5ba81"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3155,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2005302,\n        \"min\": 1101015,\n        \"max\": 9271080,\n        \"num_unique_values\": 3155,\n        \"samples\": [\n          8101026,\n          6471074,\n          7325016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nama\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 893,\n        \"min\": 0,\n        \"max\": 3090,\n        \"num_unique_values\": 3091,\n        \"samples\": [\n          1711,\n          1598,\n          2223\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"propinsi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 37,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          23,\n          26,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 127,\n        \"min\": 0,\n        \"max\": 510,\n        \"num_unique_values\": 511,\n        \"samples\": [\n          406,\n          420,\n          167\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alamat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 910,\n        \"min\": 0,\n        \"max\": 3154,\n        \"num_unique_values\": 3155,\n        \"samples\": [\n          1371,\n          1263,\n          178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jenis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          20,\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kelas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status_blu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kepemilikan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 21,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          18,\n          10,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tempat_tidur\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 550,\n        \"min\": 0,\n        \"max\": 30343,\n        \"num_unique_values\": 405,\n        \"samples\": [\n          53,\n          165,\n          283\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_layanan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 1,\n        \"max\": 419,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          124,\n          69,\n          218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tenaga_kerja\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382,\n        \"min\": 0,\n        \"max\": 7939,\n        \"num_unique_values\": 814,\n        \"samples\": [\n          384,\n          191,\n          136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-eb567e09-d93e-4dcd-8cbb-da8ee3bc11d9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>nama</th>\n",
              "      <th>propinsi</th>\n",
              "      <th>kab</th>\n",
              "      <th>alamat</th>\n",
              "      <th>jenis</th>\n",
              "      <th>kelas</th>\n",
              "      <th>status_blu</th>\n",
              "      <th>kepemilikan</th>\n",
              "      <th>total_tempat_tidur</th>\n",
              "      <th>total_layanan</th>\n",
              "      <th>total_tenaga_kerja</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1110053</td>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "      <td>210</td>\n",
              "      <td>2043</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>218</td>\n",
              "      <td>36</td>\n",
              "      <td>328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1106014</td>\n",
              "      <td>2308</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2812</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>45</td>\n",
              "      <td>15</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1171110</td>\n",
              "      <td>1946</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>2677</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>310</td>\n",
              "      <td>77</td>\n",
              "      <td>487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1171163</td>\n",
              "      <td>255</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>2079</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1102027</td>\n",
              "      <td>1881</td>\n",
              "      <td>0</td>\n",
              "      <td>250</td>\n",
              "      <td>38</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>189</td>\n",
              "      <td>34</td>\n",
              "      <td>537</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb567e09-d93e-4dcd-8cbb-da8ee3bc11d9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eb567e09-d93e-4dcd-8cbb-da8ee3bc11d9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eb567e09-d93e-4dcd-8cbb-da8ee3bc11d9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-cd3e0fd5-2233-497d-8bd9-29dd1597bd83\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cd3e0fd5-2233-497d-8bd9-29dd1597bd83')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-cd3e0fd5-2233-497d-8bd9-29dd1597bd83 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        id  nama  propinsi  kab  alamat  jenis  kelas  status_blu  \\\n",
              "0  1110053    75         0  210    2043     20      3           6   \n",
              "1  1106014  2308         0    7    2812     20      4           6   \n",
              "2  1171110  1946         0  172    2677     20      1           2   \n",
              "3  1171163   255         0  172    2079      7      1           1   \n",
              "4  1102027  1881         0  250      38     20      3           2   \n",
              "\n",
              "   kepemilikan  total_tempat_tidur  total_layanan  total_tenaga_kerja  \n",
              "0           18                 218             36                 328  \n",
              "1           18                  45             15                  45  \n",
              "2           14                 310             77                 487  \n",
              "3            3                  11             24                   0  \n",
              "4           14                 189             34                 537  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "for col in df.columns:\n",
        "    if df[col].dtype == object:\n",
        "        df[col] = encoder.fit_transform(df[col])\n",
        "\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6thO3GUscFoQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.drop(columns=[\"kelas\"])\n",
        "y = df[\"kelas\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "8rPr8tencIMN",
        "outputId": "9c8355cc-5fd9-4e71-e7cf-ee31073497aa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=200, random_state=42)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(n_estimators=200, random_state=42)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(\n",
        "    n_estimators=200,\n",
        "    max_depth=None,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "model.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwj2RVo0cLXA",
        "outputId": "1de7cc98-e83a-450c-97f8-ccc43933eced"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=== AKURASI MODEL ===\n",
            "0.8748019017432647\n",
            "\n",
            "=== CLASSIFICATION REPORT ===\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.64      0.69        14\n",
            "           1       0.83      0.70      0.76       100\n",
            "           2       0.00      0.00      0.00         1\n",
            "           3       0.88      0.94      0.91       344\n",
            "           4       0.89      0.90      0.89       155\n",
            "           5       0.82      0.53      0.64        17\n",
            "\n",
            "    accuracy                           0.87       631\n",
            "   macro avg       0.70      0.62      0.65       631\n",
            "weighted avg       0.87      0.87      0.87       631\n",
            "\n",
            "\n",
            "=== CONFUSION MATRIX ===\n",
            "[[  9   3   0   2   0   0]\n",
            " [  3  70   0  27   0   0]\n",
            " [  0   0   0   0   1   0]\n",
            " [  0  11   0 325   8   0]\n",
            " [  0   0   0  14 139   2]\n",
            " [  0   0   0   0   8   9]]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "print(\"=== AKURASI MODEL ===\")\n",
        "print(accuracy_score(y_test, y_pred))\n",
        "\n",
        "print(\"\\n=== CLASSIFICATION REPORT ===\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\n=== CONFUSION MATRIX ===\")\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "paUx2YwKcTPk",
        "outputId": "945473aa-9a7f-4f26-a58d-8321e4c3b4c0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "La6WQJ_fcWnX",
        "outputId": "f8931725-7896-430b-954a-e126059b0a81"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/model_selection/_split.py:805: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "=== CROSS VALIDATION (5-FOLD) ===\n",
            "Score per Fold: [0.87797147 0.90174326 0.86529319 0.88431062 0.61806656]\n",
            "Rata-rata Akurasi: 0.8294770206022187\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cv_scores = cross_val_score(model, X, y, cv=5)\n",
        "\n",
        "print(\"\\n=== CROSS VALIDATION (5-FOLD) ===\")\n",
        "print(\"Score per Fold:\", cv_scores)\n",
        "print(\"Rata-rata Akurasi:\", cv_scores.mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "4rnLbm1gcaXb",
        "outputId": "57877fa5-e32a-4d2e-8cd5-4acaf38817db"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "plt.plot(range(1, 6), cv_scores, marker='o')\n",
        "plt.title(\"Cross Validation Accuracy per Fold\")\n",
        "plt.xlabel(\"Fold\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": ".venv",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.12.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}