04A_ValidasiModel_RF.ipynb

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      "name": "04A.ValidasiModel_RF.ipynb",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<h2><b>Input : </b></h2>\n",
        "\n",
        "*   train_microarray.csv\n",
        "*   test_microarray.csv\n",
        "*   suspect.csv\n",
        "* rf1_features.pkl\n",
        "* rf2_features.pkl\n",
        "* rf3_features.pkl\n",
        "* rf1_model.pkl\n",
        "* rf2_model.pkl\n",
        "* rf3_model.pkl\n",
        "\n",
        "<h2><b>Output : </b></h2>\n",
        "\n",
        "*   test_microarray_prediksi_rf.csv\n",
        "*   suspect_prediksi_rf.csv"
      ],
      "metadata": {
        "id": "ViTfqQr2dURH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run All"
      ],
      "metadata": {
        "id": "0aI5ojzzdSea"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import dan Load"
      ],
      "metadata": {
        "id": "Ozrhr7KzhXXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import library yang dibutuhkan"
      ],
      "metadata": {
        "id": "A8xYn1Gm6Rmz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tWkeIwdadK_t"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import metrics\n",
        "\n",
        "import joblib"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load data"
      ],
      "metadata": {
        "id": "EA8tVIA56Tme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('https://drive.google.com/uc?id=1MiU_0V6-LvZ_CyCuWf2HoW-WXNKSIFJQ')\n",
        "df_test = pd.read_csv('https://drive.google.com/uc?id=1G-TSwysKHqTcXl0GERkf3cXKUtBrJ1Yc')\n",
        "df_suspect = pd.read_csv('https://drive.google.com/uc?id=1r8oGfgJ3RTxRZSlU7pcYkBbq2ATOoRVP')"
      ],
      "metadata": {
        "id": "wYdgawQeeiYO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load hasil seleksi fitur"
      ],
      "metadata": {
        "id": "zUwyeaIC6Vc-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_features = joblib.load('rf1_features.pkl')\n",
        "rf2_features = joblib.load('rf2_features.pkl')\n",
        "rf3_features = joblib.load('rf3_features.pkl')"
      ],
      "metadata": {
        "id": "Wx9nsMB0vWb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load hyperparameter tuning"
      ],
      "metadata": {
        "id": "vJW7OZag6XNh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_model = joblib.load('rf1_model.pkl')\n",
        "rf2_model = joblib.load('rf2_model.pkl')\n",
        "rf3_model = joblib.load('rf3_model.pkl')"
      ],
      "metadata": {
        "id": "wjb-jzV8vryq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengambil fitur dari df_train awal"
      ],
      "metadata": {
        "id": "nH4n2JGX6ZJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train1 = df_train[rf1_features]\n",
        "df_train2 = df_train[rf2_features]\n",
        "df_train3 = df_train[rf3_features]"
      ],
      "metadata": {
        "id": "fS2JtuNowoFX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengambil fitur dari df_test awal"
      ],
      "metadata": {
        "id": "d8E0z4ZA6bzz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test1 = df_test[rf1_features]\n",
        "df_test2 = df_test[rf2_features]\n",
        "df_test3 = df_test[rf3_features]"
      ],
      "metadata": {
        "id": "SMZ3RSo9wx23"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengambil fitur dari df_suspect"
      ],
      "metadata": {
        "id": "ofP2C1US6fDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_suspect1 = df_suspect[rf1_features[:-1]]\n",
        "df_suspect2 = df_suspect[rf2_features[:-1]]\n",
        "df_suspect3 = df_suspect[rf3_features[:-1]]"
      ],
      "metadata": {
        "id": "DDjLjXb9ynME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Contoh Data Train"
      ],
      "metadata": {
        "id": "xxytEywThbGG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train1.head()"
      ],
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              "   211612_s_at  219678_x_at  217653_x_at  211996_s_at  214594_x_at  \\\n",
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              "2     2.529666     2.447706     2.059190     2.805088     2.255567   \n",
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              "\n",
              "   218155_x_at  202936_s_at  207730_x_at  209703_x_at  216609_at  ...  \\\n",
              "0     2.518572     2.656989     2.833421     2.448979   1.982433  ...   \n",
              "1     2.508590     2.366057     2.871436     2.460372   2.139794  ...   \n",
              "2     2.658405     2.542071     2.871085     2.435954   2.113104  ...   \n",
              "3     2.736684     2.465318     2.988069     2.551221   2.204011  ...   \n",
              "4     2.752873     2.608392     3.098642     2.643402   2.483343  ...   \n",
              "\n",
              "   217022_s_at  214395_x_at  206835_at  221872_at  202435_s_at  205749_at  \\\n",
              "0     3.326183     2.223289   2.172511   3.372939     1.817986   2.344495   \n",
              "1     2.215997     2.310986   2.492978   3.166555     2.619466   2.850630   \n",
              "2     2.428886     2.309818   2.180694   2.794188     2.300764   2.624524   \n",
              "3     3.321267     2.512591   2.043120   2.743884     1.905358   2.407280   \n",
              "4     3.044343     2.416892   2.009510   2.612371     1.929459   2.500476   \n",
              "\n",
              "   218541_s_at  205382_s_at  217028_at  Label  \n",
              "0     2.008092     2.665495   2.582746      1  \n",
              "1     1.712133     2.541161   2.461546      1  \n",
              "2     1.660744     2.496869   2.621770      0  \n",
              "3     1.582201     2.669755   2.206625      0  \n",
              "4     1.830177     2.895747   2.974692      0  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>211612_s_at</th>\n",
              "      <th>219678_x_at</th>\n",
              "      <th>217653_x_at</th>\n",
              "      <th>211996_s_at</th>\n",
              "      <th>214594_x_at</th>\n",
              "      <th>218155_x_at</th>\n",
              "      <th>202936_s_at</th>\n",
              "      <th>207730_x_at</th>\n",
              "      <th>209703_x_at</th>\n",
              "      <th>216609_at</th>\n",
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              "      <th>217022_s_at</th>\n",
              "      <th>214395_x_at</th>\n",
              "      <th>206835_at</th>\n",
              "      <th>221872_at</th>\n",
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              "      <th>0</th>\n",
              "      <td>2.535743</td>\n",
              "      <td>2.457776</td>\n",
              "      <td>2.052387</td>\n",
              "      <td>2.873506</td>\n",
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              "      <td>2.587822</td>\n",
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              "      <td>1.957657</td>\n",
              "      <td>2.779933</td>\n",
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              "      <td>2.508590</td>\n",
              "      <td>2.366057</td>\n",
              "      <td>2.871436</td>\n",
              "      <td>2.460372</td>\n",
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              "      <td>2.215997</td>\n",
              "      <td>2.310986</td>\n",
              "      <td>2.492978</td>\n",
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              "      <td>1.712133</td>\n",
              "      <td>2.541161</td>\n",
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              "      <th>2</th>\n",
              "      <td>2.529666</td>\n",
              "      <td>2.447706</td>\n",
              "      <td>2.059190</td>\n",
              "      <td>2.805088</td>\n",
              "      <td>2.255567</td>\n",
              "      <td>2.658405</td>\n",
              "      <td>2.542071</td>\n",
              "      <td>2.871085</td>\n",
              "      <td>2.435954</td>\n",
              "      <td>2.113104</td>\n",
              "      <td>...</td>\n",
              "      <td>2.428886</td>\n",
              "      <td>2.309818</td>\n",
              "      <td>2.180694</td>\n",
              "      <td>2.794188</td>\n",
              "      <td>2.300764</td>\n",
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              "      <td>1.660744</td>\n",
              "      <td>2.496869</td>\n",
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              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2.273920</td>\n",
              "      <td>2.952965</td>\n",
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              "      <td>2.551221</td>\n",
              "      <td>2.204011</td>\n",
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              "      <td>3.321267</td>\n",
              "      <td>2.512591</td>\n",
              "      <td>2.043120</td>\n",
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              "      <td>1.905358</td>\n",
              "      <td>2.407280</td>\n",
              "      <td>1.582201</td>\n",
              "      <td>2.669755</td>\n",
              "      <td>2.206625</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>2.551101</td>\n",
              "      <td>2.692597</td>\n",
              "      <td>2.321532</td>\n",
              "      <td>2.887101</td>\n",
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              "      <td>2.752873</td>\n",
              "      <td>2.608392</td>\n",
              "      <td>3.098642</td>\n",
              "      <td>2.643402</td>\n",
              "      <td>2.483343</td>\n",
              "      <td>...</td>\n",
              "      <td>3.044343</td>\n",
              "      <td>2.416892</td>\n",
              "      <td>2.009510</td>\n",
              "      <td>2.612371</td>\n",
              "      <td>1.929459</td>\n",
              "      <td>2.500476</td>\n",
              "      <td>1.830177</td>\n",
              "      <td>2.895747</td>\n",
              "      <td>2.974692</td>\n",
              "      <td>0</td>\n",
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          "metadata": {},
          "execution_count": 8
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    {
      "cell_type": "code",
      "source": [
        "df_train1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "JrlD8rgOg_fB",
        "outputId": "73a4e1c7-dcac-4f08-c0c9-862fcd5487e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(130, 301)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Contoh Data Test"
      ],
      "metadata": {
        "id": "t2bLyadvhddJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test1.head()"
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          "metadata": {},
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    {
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      "source": [
        "### Suspect"
      ],
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        "id": "es5TB-Bbhi_i"
      }
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    {
      "cell_type": "code",
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              "      <td>...</td>\n",
              "      <td>2.859226</td>\n",
              "      <td>2.446501</td>\n",
              "      <td>2.662567</td>\n",
              "      <td>2.350769</td>\n",
              "      <td>2.507582</td>\n",
              "      <td>2.147601</td>\n",
              "      <td>2.639203</td>\n",
              "      <td>1.598737</td>\n",
              "      <td>2.871269</td>\n",
              "      <td>2.135437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.516803</td>\n",
              "      <td>2.872871</td>\n",
              "      <td>2.352785</td>\n",
              "      <td>2.557198</td>\n",
              "      <td>2.925518</td>\n",
              "      <td>3.073885</td>\n",
              "      <td>2.543978</td>\n",
              "      <td>3.295611</td>\n",
              "      <td>2.643493</td>\n",
              "      <td>2.022472</td>\n",
              "      <td>...</td>\n",
              "      <td>2.736173</td>\n",
              "      <td>2.244948</td>\n",
              "      <td>2.432493</td>\n",
              "      <td>2.293260</td>\n",
              "      <td>2.815154</td>\n",
              "      <td>2.111180</td>\n",
              "      <td>2.626987</td>\n",
              "      <td>1.677859</td>\n",
              "      <td>2.592177</td>\n",
              "      <td>2.186431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.499117</td>\n",
              "      <td>2.813643</td>\n",
              "      <td>2.559049</td>\n",
              "      <td>2.656475</td>\n",
              "      <td>2.763619</td>\n",
              "      <td>2.878468</td>\n",
              "      <td>2.592949</td>\n",
              "      <td>3.184148</td>\n",
              "      <td>2.670018</td>\n",
              "      <td>1.811895</td>\n",
              "      <td>...</td>\n",
              "      <td>2.842630</td>\n",
              "      <td>2.902077</td>\n",
              "      <td>2.636131</td>\n",
              "      <td>2.311065</td>\n",
              "      <td>2.472948</td>\n",
              "      <td>2.034676</td>\n",
              "      <td>2.572015</td>\n",
              "      <td>1.654103</td>\n",
              "      <td>2.998207</td>\n",
              "      <td>2.143887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.411571</td>\n",
              "      <td>2.756731</td>\n",
              "      <td>2.453675</td>\n",
              "      <td>2.912560</td>\n",
              "      <td>2.780565</td>\n",
              "      <td>2.861779</td>\n",
              "      <td>2.491120</td>\n",
              "      <td>3.130010</td>\n",
              "      <td>2.666348</td>\n",
              "      <td>2.357249</td>\n",
              "      <td>...</td>\n",
              "      <td>2.830770</td>\n",
              "      <td>2.802077</td>\n",
              "      <td>3.012509</td>\n",
              "      <td>2.326602</td>\n",
              "      <td>2.423827</td>\n",
              "      <td>2.101394</td>\n",
              "      <td>2.770397</td>\n",
              "      <td>1.555975</td>\n",
              "      <td>2.968291</td>\n",
              "      <td>1.992728</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 300 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d8d0985-ebb7-4fb5-943c-49646f6c9053')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7d8d0985-ebb7-4fb5-943c-49646f6c9053 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-7d8d0985-ebb7-4fb5-943c-49646f6c9053');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_suspect1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AlrqhUChfsB",
        "outputId": "1f2a73d7-1d6c-425f-8a9a-cc04aa8a3151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## X_train, y_train, X_test, y_test"
      ],
      "metadata": {
        "id": "hNl1GmybhrPl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "X_train"
      ],
      "metadata": {
        "id": "a93NFIIS6jJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train1 = df_train1.iloc[:,:-1]\n",
        "X_train2 = df_train2.iloc[:,:-1]\n",
        "X_train3 = df_train3.iloc[:,:-1]"
      ],
      "metadata": {
        "id": "MqHwQUB5h8OK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "y_train"
      ],
      "metadata": {
        "id": "cOMFovUw6lST"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train1 = df_train1.iloc[:,-1]\n",
        "y_train2 = df_train2.iloc[:,-1]\n",
        "y_train3 = df_train3.iloc[:,-1]"
      ],
      "metadata": {
        "id": "fGtq392Lh_j1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "X_test"
      ],
      "metadata": {
        "id": "JKK_3Ogg6m9i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test1 = df_test1.iloc[:,:-1]\n",
        "X_test2 = df_test2.iloc[:,:-1]\n",
        "X_test3 = df_test3.iloc[:,:-1]"
      ],
      "metadata": {
        "id": "VQ4pDhKqiGGo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "y_test"
      ],
      "metadata": {
        "id": "ZQQZ0S096oMZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_test1 = df_test1.iloc[:,-1]\n",
        "y_test2 = df_test2.iloc[:,-1]\n",
        "y_test3 = df_test3.iloc[:,-1]"
      ],
      "metadata": {
        "id": "9Y1Vak6ziNPh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validasi"
      ],
      "metadata": {
        "id": "BNrBRTxCigEn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RF1"
      ],
      "metadata": {
        "id": "86IxdyHSikhh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit model"
      ],
      "metadata": {
        "id": "9vE333Xs6qw8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_model.fit(X_train1, y_train1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzKDV4DGieGx",
        "outputId": "f59df333-741b-446f-8d24-00d1162e93eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=9, min_samples_split=4)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil prediksi dengan data train"
      ],
      "metadata": {
        "id": "5bPaXf9H9pMz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_val = rf1_model.predict(X_train1)\n",
        "print(\"Accuracy : \", metrics.accuracy_score(y_train1, rf1_val))\n",
        "print(\"Precision: \", metrics.precision_score(y_train1, rf1_val))\n",
        "print(\"Recall   : \", metrics.recall_score(y_train1, rf1_val))\n",
        "print(\"F1 Score : \", metrics.f1_score(y_train1, rf1_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27706ad8-0556-4705-c41e-afeb6b145ce3",
        "id": "eBSf7Zy39sy0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  1.0\n",
            "Precision:  1.0\n",
            "Recall   :  1.0\n",
            "F1 Score :  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi confusion matrix"
      ],
      "metadata": {
        "id": "Q2IJaUSx9sy2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = metrics.confusion_matrix(y_train1, rf1_val, labels=[1,0]).transpose()\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cf_matrix, annot=True, fmt='g', cmap='Blues', ax=ax)\n",
        "ax.set_xlabel('Aktual')\n",
        "ax.set_ylabel('Prediksi')\n",
        "ax.set_title('Confusion Matrix RF1') \n",
        "ax.xaxis.set_ticklabels(['1', '0'])\n",
        "ax.yaxis.set_ticklabels(['1', '0'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eab4011d-c1a6-4958-ad45-fcc2c0b153e6",
        "id": "mb0nffFR9sy3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, '1'), Text(0, 1.5, '0')]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil prediksi dengan data test"
      ],
      "metadata": {
        "id": "xY4Yu1BS6s3c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_prediksi = rf1_model.predict(X_test1)\n",
        "print(\"Accuracy : \", metrics.accuracy_score(y_test1, rf1_prediksi))\n",
        "print(\"Precision: \", metrics.precision_score(y_test1, rf1_prediksi))\n",
        "print(\"Recall   : \", metrics.recall_score(y_test1, rf1_prediksi))\n",
        "print(\"F1 Score : \", metrics.f1_score(y_test1, rf1_prediksi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWb8N0yKivbr",
        "outputId": "bb916bab-bf22-47b9-f81f-e475338907a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.7368421052631579\n",
            "Precision:  0.7142857142857143\n",
            "Recall   :  0.8333333333333334\n",
            "F1 Score :  0.7692307692307692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi confusion matrix"
      ],
      "metadata": {
        "id": "NkjfGKJB6vSj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = metrics.confusion_matrix(y_test1, rf1_prediksi, labels=[1,0]).transpose()\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cf_matrix, annot=True, fmt='g', cmap='Blues', ax=ax)\n",
        "ax.set_xlabel('Aktual')\n",
        "ax.set_ylabel('Prediksi')\n",
        "ax.set_title('Confusion Matrix RF1') \n",
        "ax.xaxis.set_ticklabels(['1', '0'])\n",
        "ax.yaxis.set_ticklabels(['1', '0'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWiqH-LnjPou",
        "outputId": "7cc8729a-9854-4feb-bec8-dfee9339e6b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, '1'), Text(0, 1.5, '0')]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RF2"
      ],
      "metadata": {
        "id": "qtuEAI0Z0Hxa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit model"
      ],
      "metadata": {
        "id": "cgtzXkJR6xfI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf2_model.fit(X_train2, y_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4af77b0-165b-457b-9f4e-b52d386f42d5",
        "id": "vKPuSRP70Hxo"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=6, min_samples_split=5, n_estimators=50)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil prediksi dengan data train"
      ],
      "metadata": {
        "id": "6HrhoNqT_Y5s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf2_val = rf2_model.predict(X_train2)\n",
        "print(\"Accuracy : \", metrics.accuracy_score(y_train2, rf2_val))\n",
        "print(\"Precision: \", metrics.precision_score(y_train2, rf2_val))\n",
        "print(\"Recall   : \", metrics.recall_score(y_train2, rf2_val))\n",
        "print(\"F1 Score : \", metrics.f1_score(y_train2, rf2_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "841545b4-4c0d-420b-bb3f-1200ebf66cca",
        "id": "Pi0XSx1f_Y6T"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.9923076923076923\n",
            "Precision:  0.9852941176470589\n",
            "Recall   :  1.0\n",
            "F1 Score :  0.9925925925925926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi confusion matrix"
      ],
      "metadata": {
        "id": "GTZgOd0Q_Y6U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = metrics.confusion_matrix(y_train2, rf2_val, labels=[1,0]).transpose()\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cf_matrix, annot=True, fmt='g', cmap='Blues', ax=ax)\n",
        "ax.set_xlabel('Aktual')\n",
        "ax.set_ylabel('Prediksi')\n",
        "ax.set_title('Confusion Matrix RF1') \n",
        "ax.xaxis.set_ticklabels(['1', '0'])\n",
        "ax.yaxis.set_ticklabels(['1', '0'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbd1639d-2dcb-4528-c922-bd5530c27b47",
        "id": "8I_YS8to_Y6V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, '1'), Text(0, 1.5, '0')]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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d6H2Nh8IDAAA60PuuVlqtAACA0Uk8AACgA50HHhIPAABgfBIPAADogDUeAAAAE0g8AACgA70nHgoPAADoQOd1h1YrAABgfBIPAADoQO+tVhIPAABgdBIPAADoQOeBh8IDAAB6oNUKAABgAokHAAB0oPPAQ+IBAACMT+IBAAAd6H2Nh8IDAAA60HndodUKAAAYn8QDAAA60HurlcQDAAAYncQDAAA60HngIfEAAADGJ/EAAIAO9L7GQ+EBAAAd6Lzu0GoFAACMT+IBAAAd6L3VSuIBAACMTuIBAAAd6DzwUHgAAEAPtFoBAABMIPEAAIAOSDwAAAAmkHgAAEAHOg88FB4AANADrVYAAAATSDwAAKADnQceEg8AAGB8Eg8AAOhA72s8FB4AANCBzusOrVYAAMD4JB4AANCBRZ1HHhIPAABgdBIPAADoQOeBh8QDAAAYn8QDAAA6YDtdAABgdIv6rju0WgEAAHOrqsVV9Y2q+uTw+m5VdXJVnV1VH66qrSaNofAAAIAOVNUmf2yAP0lyxqzXRyf5u9baPZNckeSwSQMoPAAAgPWqql2SPC7JO4fXleThST42XHJskoMnjWONBwAAdGCKa8vflORlSbYZXt8xyZWttdXD6/OT7DxpEIkHAAB0oMb4r2pZVZ0667HsJvesenySVa21r93c+Us8AADgFqq1tjzJ8jkueVCSJ1bVY5NsneQOSd6cZLuqWjKkHrskuWDSvSQeAADQgUW16R+TtNZe0VrbpbW2W5KnJfn31tozk3wuySHDZYcm+fjE+W/0dw4AANxSvTzJEVV1dmbWfBwz6Q1arQAAoAPT/uTy1trnk3x+eH5Okv025P0KDwAA6MCU646bTasVAAAwOokHAAB0YFHnkYfEAwAAGJ3EAwAAOtB54CHxAAAAxifxAACADkx7O92bS+EBAAAd6Lzu0GoFAACMT+IBAAAdsJ0uAADABBIPAADoQN95h8IDAAC60PuuVlqtAACA0Uk8AACgA4v6DjwkHgAAwPgkHgAA0IHe13goPAAAoAOd1x1arQAAgPFJPAAAoANbdKtVVd2htfbTqtphXedba5ePMy0AAGBLMinx+ECSxyf5WpKWm35gYkty95HmBQAAzNL7drpzFh6ttccPX++2eaYDAABsiea1uLyqHlRVtxueP6uq3lhVdxl3agAAwBpVtckfm9N8d7V6e5JrqmqvJC9O8oMk7x1tVgAAwE3UCI/Nab6Fx+rWWktyUJK3tNbemmSb8aYFAABsSea7ne5VVfWKJM9K8tCqWpTkVuNNCwAAmG1R59vpzjfxeGqS65Ic1lq7OMkuSf52tFkBAABblPkmHvu01t645kVr7UdVdduR5gQAAKyl88Bj3onHq6rq4WteVNXLMrPeAwAA2Ax639VqvonHE5N8sqpemuTAJHtE4QEAAMzTvAqP1tqlVfXEJJ/NzKeYHzLscgUAAGwGvbdazVl4VNVVSVpmtvltSbZKcvckh1RVa63dYfwpAgAAvZuz8Git+awOAABYAHrfTndS4rFHa+3Mqrrvus631r4+zrQAAIDZOq87Jq7xeHGS5yV5wzrOtSQPX8dxAACAm5jUavW84evDNs90AACAddnc299uapNarX5vrvOttX/ZtNP5lStOectYQwNssbZ/zNHTngJAd6496eXTnsItwqRWqycMX3dM8sAk/z68fliSLyUZrfAAAAB+Zb6f/L1QTWq1em6SVNVnkuzZWrtoeL1TkveMPjsAAGCLMN9PLt91TdExWJnkLiPMBwAAWIcteo3HLCuq6tNJPji8fmpmPsUcAADYDBb1XXfMr/BorR1eVU9K8tDh0PLW2nHjTQsAANiSzDfxSJKvJ7mqtfbZqrptVW3TWrtqrIkBAAC/0nviMa/F8VX1vCQfS/KPw6Gdk/zrWJMCAAC2LPNNPF6YZL8kJydJa+2sqtpxtFkBAAA3cUtZXH5da+0Xa77ZqlqSpI02KwAA4CZuEa1WSf6jql6Z5DZV9cgkH03yifGmBQAAbEnmW3i8PMklSb6d5I+SnJDkz8aaFAAAcFNVm/6xOU1staqqxUlOb63tkeQd408JAADY0kwsPFprN1TV96rqLq21H22OSQEAADe16BayuHz7JKdX1VeT/GzNwdbaE0eZFQAAcBPzXSOxUM238HjVqLMAAAC2aHMWHlW1dZLnJ7lnZhaWH9NaW705JgYAAPxK551WExObY5Psm5mi4zFJ3jD6jAAAgC3OpFarPVtr90mSqjomyVfHnxIAALC23heXT0o8rl/zRIsVAADcslTV1lX11ar6VlWdXlWvGY7frapOrqqzq+rDVbXVpLEmFR57VdVPh8dVSX57zfOq+umm+GYAAIDJpvQBgtcleXhrba8keyc5sKoekOToJH/XWrtnkiuSHDZpoDlbrVpri+c1HQAAYFSLptBp1VprSa4eXt5qeLQkD0/yjOH4sUn+Isnb5xqr9+2AAQCAEVXV4qr6ZpJVSU5K8oMkV85ainF+kp0njTPfz/EAAACmaIzF5VW1LMmyWYeWt9aWz76mtXZDkr2rarskxyXZY2PupfAAAIBbqKHIWD7xwplrr6yqzyXZP8l2VbVkSD12SXLBpPdrtQIAgA5MY3F5Vf3GkHSkqm6T5JFJzkjyuSSHDJcdmuTjk8aSeAAAQAemsbg8yU5Jjq2qxZkJLT7SWvtkVX03yYeq6q+SfCPJMZMGUngAAADr1Fo7Lck+6zh+TpL9NmQshQcAAHSgsmV/cjkAAMDNJvEAAIAOTGmNxyaj8AAAgA70XnhotQIAAEYn8QAAgA7UCJ9cvjlJPAAAgNFJPAAAoAPWeAAAAEwg8QAAgA50vsRD4QEAAD1Y1HnlodUKAAAYncQDAAA6YHE5AADABBIPAADoQOdLPBQeAADQg0Xpu/LQagUAAIxO4gEAAB3ovdVK4gEAAIxO4gEAAB3ofTtdhQcAAHTAJ5cDAABMIPEAAIAOdB54SDwAAIDxSTwAAKAD1ngAAABMIPEAAIAOdB54KDwAAKAHvbcq9T5/AACgAxIPAADoQHXeayXxAAAARifxAACADvSddyg8AACgCz7HAwAAYAKJBwAAdKDvvEPiAQAAbAYSDwAA6EDnSzwUHgAA0AOf4wEAADCBxAMAADrQe2LQ+/wBAIAOSDwAAKAD1ngAAABMIPEAAIAO9J13KDwAAKALWq0AAAAmkHgAAEAHek8Mep8/AADQAYkHAAB0oPc1HgoPAADoQN9lh1YrAABgM5B4AABABzrvtJJ4AAAA45N4AABABxZ1vspD4QEAAB3QagUAADCBwgMAADpQI/w38Z5Vu1bV56rqu1V1elX9yXB8h6o6qarOGr5uP2kshQcAALA+q5O8uLW2Z5IHJHlhVe2Z5MgkK1pruydZMbyek8IDAAA6ULXpH5O01i5qrX19eH5VkjOS7JzkoCTHDpcdm+TgSWNZXA4AAB2Y9q5WVbVbkn2SnJxkaWvtouHUxUmWTnq/xAMAAG6hqmpZVZ0667FsPdfdPsk/J/nfrbWfzj7XWmtJ2qR7STwAAKADY2yn21pbnmT53PetW2Wm6Hh/a+1fhsMrq2qn1tpFVbVTklWT7iXxAAAA1qmqKskxSc5orb1x1qnjkxw6PD80yccnjSXxAACADkzpAwQflOTZSb5dVd8cjr0yyVFJPlJVhyU5L8lTJg2k8AAAANaptfbFZL2r2h+xIWMpPAAAoAPz+cC/hUzhAQAAHVjUd91hcTkAADA+iQcAAHSg91YriQcAADA6iQcAAHRgStvpbjIKDwAA6IBWKwAAgAkkHgAA0AHb6QIAAEwg8QAAgA70vsZD4QEb4L/+8ws5+qi/zo033JgnPfn3c9jzlk17SgAL0ra3u3XefsRjsudud0pL8vzXn5CDHnyvPPYB98wvVt+QH154ZZa9/oT85GfXTXuq0I3ed7XSagXzdMMNN+S1f/2Xeds/vDPHHf+pnHjCJ/ODs8+e9rQAFqTX//Ej8plTz8neh70z+/3Ru3Lmjy7Liq+fm/s975js90fvzlkXXJ6XPv0B054msBkpPGCevvPt07LrrnfNLrvumltttVUOfOzj8vnPrZj2tAAWnDvcdqs8+D675j3/dlqS5PrVN+YnP7suK752bm64sSVJvnrGhdn5TttMc5rQnRrhsTmN1mpVVXskOSjJzsOhC5Ic31o7Y6x7wphWrVyZO+9051++3nHp0nz7tNOmOCOAhWm3nbbLpT+5Jstf+tjc5+475htnXZyXvG1Frvn59b+85g8e/dv52H/4kQBuSUZJPKrq5Uk+lJlC6qvDo5J8sKqOHOOeAMDCsGTxouy9+53zjk98I/u/4D255ufX5yVP/VVb1cuesX9uuOHGfGjFd6c4S+jPoqpN/tis8x9p3MOS/E5r7ajW2vuGx1FJ9hvOrVNVLauqU6vq1GPesXykqcHG2XHp0lx80cW/fL1q5cosXbp0ijMCWJguuOSqXHDJVTnlzIuSJMd94XvZe/eZfy+f9ajfymPvf48856hPTHOKwBSM1Wp1Y5LfTHLeWsd3Gs6tU2tteZLlSfLz1WkjzQ02yn//rfvkRz86N+ef/+Ms3XFpTjzhU/mbv33DtKcFsOCsvOJnOf+Sn2b3XXbIWedfngP2uWvOPO/SPHLfu+WIp9w/j3rxB3LtdaunPU3oTuebWo1WePzvJCuq6qwkPx6O3SXJPZMcPtI9YVRLlizJK/701XnBsj/MjTfekIOf9OTc8567T3taAAvSEW/9bN79isdnqyWLc+5FM1vnfvEth+bWt1qcTx791CQzC8z/15s/M+WZQkc6rzyqtXGChapalJnWqtmLy09prd0wn/dLPAA23PaPOXraUwDozrUnvbyLH+m/8oMrN/nPxw+4x3ab7XsfbVer1tqNSb4y1vgAAHBL0vsnl/scDwAAYHSjJR4AAMCms5l3v93kFB4AANCBzusOrVYAAMD4JB4AANCDziMPiQcAADA6iQcAAHSg9+10FR4AANCB3ne10moFAACMTuIBAAAd6DzwkHgAAADjk3gAAEAPOo88JB4AAMDoJB4AANAB2+kCAACjs50uAADABBIPAADoQOeBh8QDAAAYn8QDAAB60HnkofAAAIAO9L6rlVYrAABgdBIPAADogO10AQAAJpB4AABABzoPPBQeAADQhc4rD61WAADA6CQeAADQAdvpAgAATCDxAACADthOFwAAYAKJBwAAdKDzwEPhAQAAXei88tBqBQAAjE7iAQAAHbCdLgAAwAQKDwAA6EDVpn/M7771rqpaVVXfmXVsh6o6qarOGr5uP2kchQcAAHSgRnjM03uSHLjWsSOTrGit7Z5kxfB6TgoPAABgvVprX0hy+VqHD0py7PD82CQHTxrH4nIAAOjBwlpbvrS1dtHw/OIkSye9QeIBAAC3UFW1rKpOnfVYtqFjtNZakjbpOokHAAB0YIztdFtry5Ms34i3rqyqnVprF1XVTklWTXqDxAMAADowrV2t1uP4JIcOzw9N8vFJb1B4AAAA61VVH0zy5ST3rqrzq+qwJEcleWRVnZXkfwyv56TVCgAAOjCtteWttaev59QjNmQciQcAADA6iQcAAPRgYW2nu8EkHgAAwOgkHgAA0IExttPdnBQeAADQgZu5/e3UabUCAABGJ/EAAIAOdB54SDwAAIDxSTwAAKAHnUceCg8AAOhA77taabUCAABGJ/EAAIAO2E4XAABgAokHAAB0oPPAQ+EBAAA90GoFAAAwgcQDAAC60HfkIfEAAABGJ/EAAIAOWOMBAAAwgcQDAAA60HngofAAAIAeaLUCAACYQOIBAAAdqM6brSQeAADA6CQeAADQg74DD4UHAAD0oPO6Q6sVAAAwPokHAAB0wHa6AAAAE0g8AACgA71vp6vwAACAHvRdd2i1AgAAxifxAACADnQeeEg8AACA8Uk8AACgA7bTBQAAmEDiAQAAHbCdLgAAMDqtVgAAABMoPAAAgNEpPAAAgNFZ4wEAAB3ofY2HwgMAADrQ+65WWq0AAIDRSTwAAKADvbdaSTwAAIDRSTwAAKADnQceCg8AAOhC55WHVisAAGB0Eg8AAOiA7XQBAAAmkHgAAEAHbKcLAAAwgcQDAIn42uoAAAQASURBVAA60HngofAAAIAudF55aLUCAADWq6oOrKrvVdXZVXXkxo4j8QAAgA5MYzvdqlqc5K1JHpnk/CSnVNXxrbXvbuhYEg8AAGB99ktydmvtnNbaL5J8KMlBGzOQxAMAADowpe10d07y41mvz09y/40ZaMEWHlsv6X35DFuyqlrWWls+7XnA2q496eXTngKsk3834eYb4+fjqlqWZNmsQ8vH+t+qVivYOMsmXwLALP7dhAWotba8tbbvrMfaRccFSXad9XqX4dgGU3gAAADrc0qS3avqblW1VZKnJTl+YwZasK1WAADAdLXWVlfV4Uk+nWRxkne11k7fmLEUHrBx9CkDbBj/bkKnWmsnJDnh5o5TrbVNMB0AAID1s8YDAAAYncIDNkBVvauqVlXVd6Y9F4BeVNWBVfW9qjq7qo6c9nyA6VB4wIZ5T5IDpz0JgF5U1eIkb03ymCR7Jnl6Ve053VkB06DwgA3QWvtCksunPQ+AjuyX5OzW2jmttV8k+VCSg6Y8J2AKFB4AwJh2TvLjWa/PH44BtzAKDwAAYHQKDwBgTBck2XXW612GY8AtjMIDABjTKUl2r6q7VdVWSZ6W5PgpzwmYAoUHbICq+mCSLye5d1WdX1WHTXtOAAtZa211ksOTfDrJGUk+0lo7fbqzAqbBJ5cDAACjk3gAAACjU3gAAACjU3gAAACjU3gAAACjU3gAAACjU3gALEBVdXBVtaraY3h9QFV9ch3XHVBVD7yZ97r65rwfAOZD4QGwMD09yReHr3M5IMnNKjwAYHNQeAAsMFV1+yQPTnJYZj7lee3zv1NV36iqeyR5fpL/U1XfrKqHVNV7quqQWddevWbMqlpRVV+vqm9X1UGb6dsBgCTJkmlPAIBfc1CSE1tr36+qy6rqfmtODG1Vf5/koNbaj6rqH5Jc3Vp7/XD+sPWM+fMkT2qt/bSq7pTkK1V1fPMpsgBsJhIPgIXn6Uk+NDz/UH7VbvXfkixP8oTW2o82cMxK8tqqOi3JZ5PsnGTpJpgrAMyLxANgAamqHZI8PMl9qqolWZykJflUkouSbJ1knyQXrmeI1Rl+qVRVi5JsNRx/ZpLfSHK/1tr1VXXuMBYAbBYSD4CF5ZAk722t3bW1tltrbdckP0zykCRXJnlckr+pqgOG669Kss2s95+bZE1r1hOT3Gp4vm2SVUPR8bAkdx31uwCAtSg8ABaWpyc5bq1j/zwcT2ttZZLHJ3lrVd0/ySeSPGnN4vIk70jyu1X1rST7J/nZMMb7k+xbVd9O8gdJzhz9OwGAWcq6QgAAYGwSDwAAYHQKDwAAYHQKDwAAYHQKDwAAYHQKDwAAYHQKDwAAYHQKDwAAYHQKDwAAYHT/H6T/Ab+SeM1VAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil prediksi dengan data test"
      ],
      "metadata": {
        "id": "sOC1sZwz6z59"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf2_prediksi = rf2_model.predict(X_test2)\n",
        "print(\"Accuracy : \", metrics.accuracy_score(y_test2, rf2_prediksi))\n",
        "print(\"Precision: \", metrics.precision_score(y_test2, rf2_prediksi))\n",
        "print(\"Recall   : \", metrics.recall_score(y_test2, rf2_prediksi))\n",
        "print(\"F1 Score : \", metrics.f1_score(y_test2, rf2_prediksi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "595674bd-4806-4e37-9b85-15e89ac6b447",
        "id": "2UOR3nxz0Hxp"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.7192982456140351\n",
            "Precision:  0.7058823529411765\n",
            "Recall   :  0.8\n",
            "F1 Score :  0.7500000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi Confusion Matrix"
      ],
      "metadata": {
        "id": "z6Slyrv462bx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = metrics.confusion_matrix(y_test2, rf2_prediksi, labels=[1,0]).transpose()\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cf_matrix, annot=True, fmt='g', cmap='Blues', ax=ax)\n",
        "ax.set_xlabel('Aktual')\n",
        "ax.set_ylabel('Prediksi')\n",
        "ax.set_title('Confusion Matrix RF2') \n",
        "ax.xaxis.set_ticklabels(['1', '0'])\n",
        "ax.yaxis.set_ticklabels(['1', '0'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a2ebf00-b2bd-4daa-b7ec-970db4186e92",
        "id": "qmKSszt_0Hxq"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, '1'), Text(0, 1.5, '0')]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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uqpPXX3jG9pwTbNYuu6zJccc8IW/9h5Pz9//42dzswOvlJgfsk0+99Zk5633PywHX3zOffPMzst8+uy96qgA7rOvutXe+8+0LkyTf+faFue6eey14RjAerVabUFWf29ylJPtt7r7uXpdkXZLsesiTNc6zQ/jfz31kvvCV8/PKN/5jkuSML52bm9z/mT+7ftb7npd7PvIldrUCWMEd7nav/L+PvDcPefjj8v8+8t7c8e73XvSUgO1sqjUe+yX5jSTf2eh8JfmXib4Ttrl73P5meeThd81pZ38jJ77l6CTJc//6+HzwhM8veGYAO67/+aJn58zPfSaXfvfiPPmRD85vP3ptHvLwx+aVL3hmPvaB47Pv9W+Qpzz7RYueJgxn9O10a4odearq2CR/O3vE+sbX3tzdvztvDIkHwJY74V0vXPQUAIZzx4P2GOI3+psf9Q/b/Pfjf3vpg7bbzz5J4tHdR65wbW7RAQAAXNHggYftdAEAYASjt1p5gCAAADA5iQcAAAxg8MBD4gEAAExP4gEAAAMYfY2HwgMAAAYweN2h1QoAAJiexAMAAAawZs3YkYfEAwAAmJzEAwAABjD6Gg+FBwAADGD0Xa20WgEAAJOTeAAAwAAGDzwkHgAAwPQkHgAAMABrPAAAAOaQeAAAwABGTzwUHgAAMIDB6w6tVgAAwPQkHgAAMIDRW60kHgAAwOQkHgAAMIDBAw+FBwAAjECrFQAAwBwSDwAAGMDggYfEAwAAmJ7EAwAABmCNBwAAMLmqbX+s7nvrtVV1QVWdvuzc7avqxKo6tapOrqq7zBtH4QEAAKzkdUkO2+jcS5I8r7tvn+Q5s/cr0moFAAADWFSrVXd/oqoO2vh0kj1mr6+b5Nx54yg8AACALfXUJB+sqmOy1EV1j3k3aLUCAIABTLHGo6rWztZobDjWrnI6f5Dkj7v7Rkn+OMmx826QeAAAwFVUd69Lsm4rbn1skqfMXr89yWvm3SDxAACAAVTVNj+uhHOT3Hv2+n5JvjjvBokHAAAMYFGP8aiq45LcJ8m+VXVOkucmeUKSV1TVLkl+lGRui5bCAwAA2KzufsRmLt1xS8ZReAAAwAA8uRwAAGAOiQcAAAxg8MBD4QEAACPQagUAADCHxAMAAAYg8QAAAJhD4gEAAAMYPPBQeAAAwAi0WgEAAMwh8QAAgAEMHnhIPAAAgOlJPAAAYACjr/FQeAAAwAAGrzu0WgEAANOTeAAAwADWDB55SDwAAIDJSTwAAGAAgwceEg8AAGB6Eg8AABiA7XQBAIDJrRm77tBqBQAATE/iAQAAAxi91UriAQAATE7iAQAAAxg88FB4AADACCpjVx5arQAAgMlJPAAAYAC20wUAAJhD4gEAAAMYfTtdhQcAAAxg8LpDqxUAADA9iQcAAAxgzeCRh8QDAACYnMQDAAAGMHjgIfEAAACmJ/EAAIAB2E4XAACY3OB1h1YrAABgegoPAAAYwJqqbX6sRlW9tqouqKrTNzr/R1V1VlWdUVUvmTv/rfy5AQCAq4bXJTls+Ymqum+SI5Lcrrt/Nckx8waxxgMAAAawqCUe3f2Jqjpoo9N/kOTF3f3j2WcumDeOxAMAAAZQVdv8uBJ+OcmhVXVSVf1TVd153g0KDwAAuIqqqrVVdfKyY+0qb90lyd5J7pbkaUneVnMqGa1WAAAwgDUT9Fp197ok67bi1nOSvLO7O8mnquqnSfZN8q3N3SDxAAAAttS7k9w3Sarql5NcI8mFK90g8QAAgAEs6snlVXVckvsk2beqzkny3CSvTfLa2Ra7lyV57Cz92CyFBwAADGBRTy7v7kds5tKjtmQcrVYAAMDkJB4AADCARbVabSsrFh5VtUd3X1JVe2/qendfNM20AACAncm8xOPNSQ5P8pkknSs+MLGT3GyieQEAAMtMsZ3u9rRi4dHdh8/+vOn2mQ4AALAzWtXi8qq6Z1Vde/b6UVX1sqq68bRTAwAANqiqbX5sT6vd1epvkvygqm6X5Kgk/5bkDZPNCgAAuIKa4NieVlt4rJ89EOSIJH/d3a9Ksvt00wIAAHYmq91O99KqemaWHhJyr6pak+Tq000LAABYbs3g2+muNvF4eJIfJzmyu89PcmCSv5xsVgAAwE5ltYnHId39sg1vuvtrVbXbRHMCAAA2MnjgserE48+r6n4b3lTV07O03gMAANgORt/VarWJx0OSvLeqnpbksCS3isIDAABYpVUVHt19YVU9JMlHsvQU84fNdrkCAAC2g9FbrVYsPKrq0iSdpW1+O8k1ktwsycOqqrt7j+mnCAAAjG7FwqO7PasDAAB2AKNvpzsv8bhVd59VVXfY1PXuPmWaaQEAAMsNXnfMXeNxVJInJHnpJq51kvtt4jwAAMAVzGu1esLsz/tun+kAAACbsr23v93W5rVa/ceVrnf3O7ftdH7uO5/+66mGBthpHbHupEVPAWA4H/zDuy56ClcJ81qtfnP25/WT3CPJP87e3zfJvySZrPAAAAB+brVP/t5RzWu1enySVNWHkhzc3efN3u+f5HWTzw4AANgprPbJ5TfaUHTMfDPJjSeYDwAAsAk79RqPZT5aVR9Mctzs/cOz9BRzAABgO1gzdt2xusKju59cVb+V5F6zU+u6+13TTQsAANiZrDbxSJJTklza3R+pqt2qavfuvnSqiQEAAD83euKxqsXxVfWEJH+X5P/MTh2Q5N1TTQoAANi5rDbxeFKSuyQ5KUm6+4tVdf3JZgUAAFzBVWVx+Y+7+7INP2xV7ZKkJ5sVAABwBVeJVqsk/1RVz0qya1U9IMnbk7xnumkBAAA7k9UWHs9I8q0kpyX5/STvT/JnU00KAAC4oqptf2xPc1utqupqSc7o7lslefX0UwIAAHY2cwuP7v5JVX2hqm7c3V/bHpMCAACuaM1VZHH5XknOqKpPJfn+hpPd/ZBJZgUAAFzBatdI7KhWW3j8+aSzAAAAdmorFh5Vda0kT0zyS1laWH5sd6/fHhMDAAB+bvBOq7mJzeuT3ClLRceDkrx08hkBAAA7nXmtVgd3922SpKqOTfKp6acEAABsbPTF5fMSj8s3vNBiBQAAbK15icftquqS2evK0pPLL5m97u7eY9LZAQAASXbyNR7dfbXu3mN27N7duyx7regAAIDtZE1t+2M1quq1VXVBVZ2+iWtHVVVX1b5z57/lPzIAAHAV8rokh218sqpulOSBSVb1kHGFBwAADGBN1TY/VqO7P5Hkok1c+qskT0/Sq5r/qn9SAACAJFV1RJJvdPdnV3vPap9cDgAALNAUi8uram2StctOrevudXPu2S3Js7LUZrVqCg8AABjAaheDb4lZkbFiobEJN09y0ySfraVq6MAkp1TVXbr7/M3dpPAAAABWrbtPS3L9De+r6qtJ7tTdF650nzUeAAAwgJrgf6v63qrjknwyyS2r6pyqOnJr5i/xAAAANqu7HzHn+kGrGUfhAQAAA5hijcf2pPAAAIABjF54WOMBAABMTuIBAAADqCke5LEdSTwAAIDJSTwAAGAA1ngAAADMIfEAAIABDL7EQ+EBAAAjWDN45aHVCgAAmJzEAwAABmBxOQAAwBwSDwAAGMDgSzwUHgAAMII1Gbvy0GoFAABMTuIBAAADGL3VSuIBAABMTuIBAAADGH07XYUHAAAMwJPLAQAA5pB4AADAAAYPPCQeAADA9CQeAAAwAGs8AAAA5pB4AADAAAYPPBQeAAAwgtFblUafPwAAMACJBwAADKAG77WSeAAAAJOTeAAAwADGzjsUHgAAMATP8QAAAJhD4gEAAAMYO++QeAAAANuBxAMAAAYw+BIPhQcAAIzAczwAAADmkHgAAMAARk8MRp8/AAAwAIUHAAAMoKq2+bHK731tVV1QVacvO/eXVXVWVX2uqt5VVXvOG0fhAQAArOR1SQ7b6NyHk9y6u2+b5Owkz5w3iMIDAAAGUBMcq9Hdn0hy0UbnPtTd62dvT0xy4LxxLC4HAIAB7MDb6f5ekrfO+5DEAwAArqKqam1VnbzsWLuF9z87yfokb5r3WYkHAAAMYIrEoLvXJVm3NfdW1eOSHJ7k/t3d8z6v8AAAALZIVR2W5OlJ7t3dP1jNPQoPAAAYwKLWeFTVcUnuk2TfqjonyXOztIvVNZN8eDavE7v7iSuNo/AAAIABLGppeXc/YhOnj93ScSwuBwAAJifxAACAAey4u+mujsQDAACYnMQDAAAGsGZhqzy2DYUHAAAMQKsVAADAHBIPAAAYQA3eaiXxAAAAJifxAACAAYy+xkPhAQAAAxh9VyutVgAAwOQkHgAAMIDRW60kHgAAwOQkHgAAMACJBwAAwBwSDwAAGMDoDxBUeAAAwADWjF13aLUCAACmJ/EAAIABjN5qJfEAAAAmJ/EAAIABjL6drsIDAAAGoNUKAABgDokHAAAMwHa6AAAAc0g8AABgAKOv8VB4wBa45JJL8rzn/Fm+9KWzU1V53l+8MLe7/SGLnhbADuVP7nvT3PUme+XiH16e33/raUmSZz3wl3LgntdKklz7Grvk+5etzx++7fRFThOGY1cruAp5yYtekHv+2qF56ctfmcsvuyw//NGPFj0lgB3Oh866MMef9s087f43/9m5F37oSz97vfYeN873L/vJIqYGLJA1HrBKl156aT7zmU/nt377YUmSq1/jGtljjz0WPCuAHc/p512aS3+8frPX7/VLe+djX7xwO84Idg41wbE9TZZ4VNWtkhyR5IDZqW8kOb67z5zqO2FK3zjnnOy11955zrOfmS984awc/Ku/mqcf/ezstttui54awDBuvf/u+c4PLs+53/3xoqcCbGeTJB5V9Ywkb8lSIfWp2VFJjquqo6f4TpjaT36yPmed+fn8p995RN72jndn1113zWtfs27R0wIYyn1vsU8+/sVvL3oaMKQ1Vdv82K7zn2jcI5Pcubtf3N1vnB0vTnKX2bVNqqq1VXVyVZ187Kv9QseOZb/9bpD99rtBbnvb2yVJHvDAw3LWmZ9f8KwAxrGmknvebO/805cuWvRUgAWYqtXqp0lumOTfNzq//+zaJnX3uiTrkuRH69MTzQ22yr7Xu172u8EN8tWvfDkH3fRmOenET+ZmN7/5/BsBSJLc4cDr5uvf+WEu/P5li54KDGnwTa0mKzyemuSjVfXFJF+fnbtxkl9K8uSJvhMmd/Sz/jzPfMaf5vLLL8+BB94oz//vL1r0lAB2OEc/4Oa57Q33yHWvtUve+JhD8oZPn5MPnvmt3PsW++TjX9JmBVtt8MqjuqcJFqpqTZZaq5YvLv90d69q/zyJB8CWO2LdSYueAsBwPviHdx3iV/oT/+3ibf778d1uvud2+9kn29Wqu3+a5MSpxgcAgKuS0Z9c7jkeAADA5Dy5HAAABrCdd7/d5hQeAAAwgMHrDq1WAADA5lXVa6vqgqo6fdm5vavqw1X1xdmfe80bR+EBAAAjqAmO1XldksM2Ond0ko929y2SfHT2fkUKDwAAYLO6+xNJLtro9BFJXj97/fokD503jjUeAAAwgB1sO939uvu82evzk+w37waJBwAADKBqiqPWVtXJy461WzqvXnoi+dyHG0o8AADgKqq71yVZtxW3frOq9u/u86pq/yQXzLtB4gEAAANY3NryTTo+yWNnrx+b5O/n3aDwAAAANquqjkvyySS3rKpzqurIJC9O8oCq+mKSX5+9X5FWKwAAGMGC1pZ39yM2c+n+WzKOxAMAAJicxAMAAAawg22nu8UUHgAAMIAau+7QagUAAExP4gEAAAMYPPCQeAAAANOTeAAAwAgGjzwUHgAAMIDRd7XSagUAAExO4gEAAAOwnS4AAMAcEg8AABjA4IGHwgMAAIYweOWh1QoAAJicxAMAAAZgO10AAIA5JB4AADAA2+kCAADMIfEAAIABDB54KDwAAGAIg1ceWq0AAIDJSTwAAGAAttMFAACYQ+IBAAADGH07XYUHAAAMYPC6Q6sVAAAwPYkHAACMYPDIQ+IBAABMTuIBAAADGH07XYUHAAAMYPRdrbRaAQAAk5N4AADAAAYPPCQeAADA9CQeAAAwgsEjD4kHAAAwOYkHAAAMwHa6AADA5GynCwAAMIfEAwAABjB44CHxAAAApqfwAACAEUYVpXcAAAX8SURBVNQEx2q+tuqPq+qMqjq9qo6rqmttzfQVHgAAMICa4H9zv7PqgCT/NcmduvvWSa6W5He2Zv4KDwAAYCW7JNm1qnZJsluSc7dmEIUHAAAMoGqKo9ZW1cnLjrXLv7O7v5HkmCRfS3Jeku9294e2Zv52tQIAgKuo7l6XZN3mrlfVXkmOSHLTJBcneXtVPaq737il3yXxAACAASxobfmvJ/lKd3+ruy9P8s4k99ia+Us8AABgAAt6cvnXktytqnZL8sMk909y8tYMJPEAAAA2qbtPSvJ3SU5JclqW6ofNtmatROIBAABDWEzk0d3PTfLcKzuOxAMAAJicxAMAAAawoDUe24zEAwAAmJzEAwAABjB44KHwAACAEWi1AgAAmEPiAQAAA6jBm60kHgAAwOQkHgAAMIKxAw+FBwAAjGDwukOrFQAAMD2JBwAADMB2ugAAAHNIPAAAYACjb6er8AAAgBGMXXdotQIAAKYn8QAAgAEMHnhIPAAAgOlJPAAAYAC20wUAAJhD4gEAAAOwnS4AADA5rVYAAABzKDwAAIDJKTwAAIDJWeMBAAADGH2Nh8IDAAAGMPquVlqtAACAyUk8AABgAKO3Wkk8AACAyUk8AABgAIMHHgoPAAAYwuCVh1YrAABgchIPAAAYgO10AQAA5pB4AADAAGynCwAAMIfEAwAABjB44KHwAACAIQxeeWi1AgAANquq9qyqv6uqs6rqzKq6+9aMI/EAAIABLHA73Vck+UB3P6yqrpFkt60ZROEBAABsUlVdN8m9kjwuSbr7siSXbc1YWq0AAGAAVdv+WIWbJvlWkr+tqn+tqtdU1bW3av7dvTX3wVVaVa3t7nWLngfAKPx3E3ZMVbU2ydplp9Yt/7/VqrpTkhOT3LO7T6qqVyS5pLv/fIu/S+EBW66qTu7uOy16HgCj8N9NGFNV3SDJid190Oz9oUmO7u4Hb+lYWq0AAIBN6u7zk3y9qm45O3X/JJ/fmrEsLgcAAFbyR0neNNvR6stJHr81gyg8YOvoUwbYMv67CYPq7lOTXOlWSWs8AACAyVnjAQAATE7hAVugql5bVRdU1emLngvAKKrqsKr6QlV9qaqOXvR8gMVQeMCWeV2SwxY9CYBRVNXVkrwqyYOSHJzkEVV18GJnBSyCwgO2QHd/IslFi54HwEDukuRL3f3l7r4syVuSHLHgOQELoPAAAKZ0QJKvL3t/zuwccBWj8AAAACan8AAApvSNJDda9v7A2TngKkbhAQBM6dNJblFVN5099fh3khy/4DkBC6DwgC1QVccl+WSSW1bVOVV15KLnBLAj6+71SZ6c5INJzkzytu4+Y7GzAhbBk8sBAIDJSTwAAIDJKTwAAIDJKTwAAIDJKTwAAIDJKTwAAIDJKTwAdkBV9dCq6qq61ez9farqvZv43H2q6h5X8ru+d2XuB4DVUHgA7JgekeSE2Z8ruU+SK1V4AMD2oPAA2MFU1XWS/FqSI7P0lOeNr9+5qv61qm6e5IlJ/riqTq2qQ6vqdVX1sGWf/d6GMavqo1V1SlWdVlVHbKcfBwCSJLssegIA/IIjknygu8+uqm9X1R03XJi1Vf3PJEd099eq6n8n+V53HzO7fuRmxvxRkt/q7kuqat8kJ1bV8e0psgBsJxIPgB3PI5K8Zfb6Lfl5u9WvJFmX5De7+2tbOGYleWFVfS7JR5IckGS/bTBXAFgViQfADqSq9k5yvyS3qapOcrUkneR9Sc5Lcq0khyQ5dzNDrM/sL5Wqak2Sa8zOPzLJ9ZLcsbsvr6qvzsYCgO1C4gGwY3lYkjd09026+6DuvlGSryQ5NMnFSR6c5EVVdZ/Z5y9Nsvuy+7+aZENr1kOSXH32+rpJLpgVHfdNcpNJfwoA2IjCA2DH8ogk79ro3Dtm59Pd30xyeJJXVdVdk7wnyW9tWFye5NVJ7l1Vn01y9yTfn43xpiR3qqrTkjwmyVmT/yQAsExZVwgAAExN4gEAAExO4QEAAExO4QEAAExO4QEAAExO4QEAAExO4QEAAExO4QEAAExO4QEAAEzu/wNDYaGbZq0jqAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RF3"
      ],
      "metadata": {
        "id": "duiJ97s80K-W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit model"
      ],
      "metadata": {
        "id": "W8gnfwZF7Ps3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf3_model.fit(X_train3, y_train3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9ac9e8d-a2c4-44ad-aace-4cd5a0a24647",
        "id": "lhUX1wsz0K-X"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil prediksi dengan data train"
      ],
      "metadata": {
        "id": "V8QQ9chV_-oo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf3_val = rf3_model.predict(X_train3)\n",
        "print(\"Accuracy : \", metrics.accuracy_score(y_train3, rf3_val))\n",
        "print(\"Precision: \", metrics.precision_score(y_train3, rf3_val))\n",
        "print(\"Recall   : \", metrics.recall_score(y_train3, rf3_val))\n",
        "print(\"F1 Score : \", metrics.f1_score(y_train3, rf3_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a126b37e-bc66-458a-9443-77edf3dc7d62",
        "id": "dD9Y2CMR_-o5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  1.0\n",
            "Precision:  1.0\n",
            "Recall   :  1.0\n",
            "F1 Score :  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi confusion matrix"
      ],
      "metadata": {
        "id": "VQboBTko_-o5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = metrics.confusion_matrix(y_train3, rf3_val, labels=[1,0]).transpose()\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cf_matrix, annot=True, fmt='g', cmap='Blues', ax=ax)\n",
        "ax.set_xlabel('Aktual')\n",
        "ax.set_ylabel('Prediksi')\n",
        "ax.set_title('Confusion Matrix RF1') \n",
        "ax.xaxis.set_ticklabels(['1', '0'])\n",
        "ax.yaxis.set_ticklabels(['1', '0'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a02fd8fd-808f-4b04-ca13-0f6e30f235e0",
        "id": "hNKoUKZk_-o6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, '1'), Text(0, 1.5, '0')]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediksi dengan data test"
      ],
      "metadata": {
        "id": "QmjUI_Gj7RUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf3_prediksi = rf3_model.predict(X_test3)\n",
        "print(\"Accuracy : \", metrics.accuracy_score(y_test3, rf3_prediksi))\n",
        "print(\"Precision: \", metrics.precision_score(y_test3, rf3_prediksi))\n",
        "print(\"Recall   : \", metrics.recall_score(y_test3, rf3_prediksi))\n",
        "print(\"F1 Score : \", metrics.f1_score(y_test3, rf3_prediksi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7fb9a0c-c9b0-4752-d0c2-ec601e52bf08",
        "id": "PylLDHu-0K-Y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.7719298245614035\n",
            "Precision:  0.7297297297297297\n",
            "Recall   :  0.9\n",
            "F1 Score :  0.8059701492537312\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi Confusion Matrix"
      ],
      "metadata": {
        "id": "8SrREwd57TTF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = metrics.confusion_matrix(y_test3, rf3_prediksi, labels=[1,0]).transpose()\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "ax = plt.subplot()\n",
        "sns.heatmap(cf_matrix, annot=True, fmt='g', cmap='Blues', ax=ax)\n",
        "ax.set_xlabel('Aktual')\n",
        "ax.set_ylabel('Prediksi')\n",
        "ax.set_title('Confusion Matrix RF3') \n",
        "ax.xaxis.set_ticklabels(['1', '0'])\n",
        "ax.yaxis.set_ticklabels(['1', '0'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87a61462-90ea-41ab-a160-eebfda3b1e31",
        "id": "CBFQ7IVC0K-Y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, '1'), Text(0, 1.5, '0')]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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Ydi+NBwAAdKCqNvhjAVYkOba1tmeSxyV5eVXtmeS4JGe11h6W5Kzh+3lpPAAAgNVqrV3ZWjt3eL48yTeT7Jbk0CSnDJedkuTZ0+5lcTkAAHRg1tvpVtUeSR6T5Jwku7TWrhxOXZVkl2mvl3gAAMDdVFUdXVXL5jyOXsN1Wyc5I8nvtdZunHuutdaStGnvJfEAAIAOjPHJ5a21pUmWTnnfe2TSdLyztfa+4fDVVbVra+3Kqto1yTXT3kviAQAArFZNup0Tk3yztfbGOac+kOSI4fkRSd4/7V4SDwAA6MAYiccCPDHJS5KcX1XnDcdeneT1Sd5TVUcl+V6S5027kcYDAAA6MIu+o7V2dpI1vfMvrc29jFoBAACjk3gAAEAHZjRqtcFIPAAAgNFJPAAAoAOdBx4aDwAA6IFRKwAAgCkkHgAA0IHOAw+JBwAAMD6JBwAAdKD3NR4aDwAA6EDnfYdRKwAAYHwSDwAA6EDvo1YSDwAAYHQSDwAA6EDngYfEAwAAGJ/EAwAAOtD7Gg+NBwAAdKDzvsOoFQAAMD6JBwAAdKD3USuJBwAAMDqJBwAAdKDzwEPjAQAAPTBqBQAAMIXEAwAAOiDxAAAAmELiAQAAHeg88NB4AABAD4xaAQAATCHxAACADnQeeEg8AACA8Uk8AACgA72v8dB4AABABzrvO4xaAQAA45N4AABAB5Z0HnlIPAAAgNFJPAAAoAOdBx4SDwAAYHwSDwAA6IDtdAEAgNEt6bvvMGoFAACMT+IBAAAd6H3USuIBAACMTuIBAAAd6Dzw0HgAAEAPKn13HkatAACA0Uk8AACgA7bTBQAAmELiAQAAHeh9O12NBwAAdKDzvsOoFQAAMD6JBwAAdGBJ55GHxAMAABidxAMAADrQeeAh8QAAAMYn8QAAgA7YThcAABhd532HUSsAAGB8Eg8AAOiA7XQBAACmkHgAAEAH+s47NB4AANCF3ne1MmoFAACMTuIBAAAdWNJ34CHxAAAAxifxAACADvS+xkPjAQAAHei87zBqBQAAjE/iAQAAHdikR62qatvW2o1VtePqzrfWrh+nLAAAYFMyLfF4V5JDknw1SctdPzCxJXnwSHUBAABz9L6d7ryNR2vtkOHrgxanHAAAYFO0oMXlVfXEqtpqeP7iqnpjVe0+bmkAAMBKVbXBH4tpobtavTXJj6tqryTHJvlWklNHqwoAALiLGuGxmBbaeKxorbUkhyZ5c2vtLUm2Ga8sAABgU7LQ7XSXV9X/SPLiJE+uqiVJ7jFeWQAAwFxLOt9Od6GJx/OT3JrkqNbaVUkekOSvRqsKAADYpCw08XhMa+2NK79prV1aVVuOVBMAALCKzgOPBScef1xVT135TVX9YSbrPQAAgEXQ+65WC008npXkQ1X1qiTPSPKIaDwAAIAFWlDj0Vq7tqqeleRTmXyK+XOHXa4AAIBF0Puo1byNR1UtT9Iy2ea3JdkiyYOTPLeqWmtt2/FLBAAAejdv49Fa81kdAACwEeh9O91piccjWmsXVdUvrO58a+3cccoCAADmmlXfUVUnJTkkyTWttZ8fjv1Jkpcl+cFw2atbax+Z7z7T1ngcO9zwDas515I8dTXHAQCATcfJSd6c5B9WOf7XrbUTFnqTaaNWLxu+Hri21QEAABvOYm9/u1Jr7XNVtcf63mfaqNVzphTxvvUtYE1++JU3j3VrgE3WC05eNusSALpz0CN3nnUJM1NVRyc5es6hpa21pQt8+TFV9dIky5Ic21r74XwXTxu1+pXh632TPCHJp4fvD0zyhSSjNR4AAMB/Wugnf6+NoclYaKMx11uTvC6T5Revy2Rpxm/M94Jpo1ZHJklVfSLJnq21K4fvd81k1gsAALibaa1dvfJ5Vb0tyYemvWahn1z+wJVNx+DqJLuvXXkAAMC6mtUaj9Wpql3n9AeHJblg2msW2nicVVUfT3L68P3zM/kUcwAAYBEsmd12uqcnOSDJTlV1WZLjkxxQVXtnMmr13SS/Ne0+C2o8WmvHVNVhSZ48HFraWjtzHeoGAAA60lo7fDWHT1zb+yw08UiSc5Msb619qqq2rKptWmvL1/YNAQCAtTerxGNDWdDi+Kp6WZJ/SvL/hkO7JfnnsYoCAAA2LQtNPF6eZN8k5yRJa+2SqrrvaFUBAAB3sTEtLl8XC208bm2t/XTlD1tVm2eykAQAAFgEd4tRqyT/UlWvTnLvqnpakvcm+eB4ZQEAAJuShTYef5TkB0nOz2SrrI8kec1YRQEAAHdVteEfi2nqqFVVbZbkwtbaI5K8bfySAACATc3UxqO1dntV/XtV7d5au3QxigIAAO5qyd1kcfkOSS6sqi8nuXnlwdbas0apCgAAuIuFrpHYWC208fjjUasAAAA2afM2HlV1ryS/neShmSwsP7G1tmIxCgMAAP5T55NWUxObU5Lsk0nTcXCSN4xeEQAAsMmZNmq1Z2vtUUlSVScm+fL4JQEAAKvqfXH5tMTjtpVPjFgBAADralrisVdV3Tg8r0w+ufzG4XlrrW07anUAAECS/td4zNt4tNY2W6xCAACANVvSeePR+3bAAABABxb6OR4AAMAMbeqLywEAANabxAMAADrQeeCh8QAAgB5YXA4AADCFxAMAADpQ6TvykHgAAACjk3gAAEAHel/jofEAAIAO9N54GLUCAABGJ/EAAIAOVOcf5CHxAAAARifxAACADljjAQAAMIXEAwAAOtD5Eg+NBwAA9GBJ552HUSsAAGB0Eg8AAOiAxeUAAABTSDwAAKADnS/x0HgAAEAPlqTvzsOoFQAAMDqJBwAAdKD3USuJBwAAMDqJBwAAdKD37XQ1HgAA0AGfXA4AADCFxAMAADrQeeAh8QAAAMYn8QAAgA5Y4wEAADCFxAMAADrQeeCh8QAAgB70PqrUe/0AAEAHJB4AANCB6nzWSuIBAACMTuIBAAAd6Dvv0HgAAEAXfI4HAADAFBIPAADoQN95h8QDAABYBBIPAADoQOdLPDQeAADQA5/jAQAAMIXEAwAAOtB7YtB7/QAAQAckHgAA0AFrPAAAAKaQeAAAQAf6zjs0HgAA0AWjVgAAAFNIPAAAoAO9Jwa91w8AAHRA4gEAAB3ofY2HxgMAADrQd9th1AoAAFgEEg8AAOhA55NWEg8AAGB8Eg8AAOjAks5XeWg8AACgA0atAAAAppB4AABAB6rzUSuJBwAAMDqJBwAAdMAaDwAAYHRLUhv8sRBVdVJVXVNVF8w5tmNVfbKqLhm+7jC9fgAAgDU7OckzVjl2XJKzWmsPS3LW8P28NB4AANCBqg3/WIjW2ueSXL/K4UOTnDI8PyXJs6fdR+MBAACsrV1aa1cOz69Kssu0F2g8AACgA2MkHlV1dFUtm/M4em3raq21JG3adXa1AgCAu6nW2tIkS9fhpVdX1a6ttSuratck10x7gcQDAAA6UCP8bz18IMkRw/Mjkrx/2gskHgAA0IElM/ocj6o6PckBSXaqqsuSHJ/k9UneU1VHJflekudNu4/GAwAAWKPW2uFrOPVLa3MfjQcAAHRgPUejZs4aDwAAYHQSDwAA6MBCP/BvY6XxAACADhi1AgAAmELiAQAAHZjVdrobisQDAAAYncQDAAA60PsaD40HLNCtt96aI1/6otz2059mxe2352kHPT2/e8x/n3VZABudY/bfI/vsvl1+9JMVecX7LkyS/MFTH5zdtrtXkmSrLTbLzT+9Pb9/5jdmWSZ0x65WcDexxRZb5O0nnZItt9oqt912W379JS/Mk/Z/ch69196zLg1go/LpS67NR75xTV7xlAfdeeyET3/7zudH7veA3PzT22dRGjBD1njAAlVVttxqqyTJihUrsmLFiv7/6QFgBN+46qbcdOuKNZ5/4oN2zOe/df0iVgSbhhrhsZhGSzyq6hFJDk2y23Do8iQfaK19c6z3hLHdfvvtOfzXnpNLL700zz/8hXn0o/eadUkAXdnzflvnhp/clitvvHXWpQCLbJTEo6r+KMm7M2mkvjw8KsnpVXXcGO8Ji2GzzTbLe973/nzi0/+SC87/ei655OJZlwTQlf0fsmM+/21pB6yLJVUb/LGo9Y9036OS/GJr7fWttdOGx+uT7DucW62qOrqqllXVshPftnSk0mD9bbvttvnFfffLF87+/KxLAejGkkoev8cOOduYFdwtjTVqdUeS+yf53irHdx3OrVZrbWmSpUlyy4q0kWqDdXL99ddn8803z7bbbptbbrklX/riF3LkUS+bdVkA3dhrt21z2Q235Lof3zbrUqBLva8sHavx+L0kZ1XVJUm+PxzbPclDkxwz0nvCqK79wTV5zauPyx133J477mg56OnPyFMOOHDWZQFsdF554IPy87tuk23vtXnefvij8+6vXpFPXXxt9n+wReWwXjrvPKq1cYKFqlqSyWjV3MXlX2mtLWj/PIkHwNp7wcnLZl0CQHf++Tf36eJX+i9964YN/vvx4x6y/aL97KPtatVauyPJl8a6PwAA3J30/snlPscDAAAYnU8uBwCADvT+ucUaDwAA6EDnfYdRKwAAYHwSDwAA6EHnkYfEAwAAGJ3EAwAAOtD7droaDwAA6EDvu1oZtQIAAEYn8QAAgA50HnhIPAAAgPFJPAAAoAedRx4SDwAAYHQSDwAA6IDtdAEAgNHZThcAAGAKiQcAAHSg88BD4gEAAIxP4gEAAD3oPPLQeAAAQAd639XKqBUAADA6iQcAAHTAdroAAABTSDwAAKADnQceGg8AAOhC552HUSsAAGB0Eg8AAOiA7XQBAACmkHgAAEAHbKcLAAAwhcQDAAA60HngofEAAIAudN55GLUCAABGJ/EAAIAO2E4XAABgCokHAAB0oPftdDUeAADQgc77DqNWAADA+CQeAADQg84jD4kHAAAwOokHAAB0oPftdDUeAADQgd53tTJqBQAAjE7iAQAAHeg88JB4AAAA45N4AABADzqPPCQeAADA6CQeAADQAdvpAgAAo7OdLgAAwBQSDwAA6EDngYfEAwAAGJ/EAwAAetB55KHxAACADvS+q5VRKwAAYHQSDwAA6IDtdAEAAKaQeAAAQAc6Dzw0HgAA0AOjVgAAAFNIPAAAoAt9Rx4SDwAAYHQSDwAA6IA1HgAAAFNIPAAAoAOzCjyq6rtJlie5PcmK1to+63IfjQcAAHRgxqNWB7bWrl2fGxi1AgAARqfxAACADtQI/1ugluQTVfXVqjp6Xes3agUAAHdTQyMxt5lY2lpbusplT2qtXV5V903yyaq6qLX2ubV9L40HAAD0YIQ1HkOTsWqjseo1lw9fr6mqM5Psm2StGw+jVgAA0IEa4TH1Pau2qqptVj5PclCSC9alfokHAACwJrskObMmW2ptnuRdrbWPrcuNNB4AANCBWWyn21r7dpK9NsS9jFoBAACjk3gAAEAH1mL7242SxgMAAHrQd99h1AoAABifxAMAADrQeeAh8QAAAMYn8QAAgA7MYjvdDUniAQAAjE7iAQAAHbCdLgAAMDqjVgAAAFNoPAAAgNFpPAAAgNFZ4wEAAB3ofY2HxgMAADrQ+65WRq0AAIDRSTwAAKADvY9aSTwAAIDRSTwAAKADnQceGg8AAOhC552HUSsAAGB0Eg8AAOiA7XQBAACmkHgAAEAHbKcLAAAwhcQDAAA60HngofEAAIAudN55GLUCAABGJ/EAAIAO2E4XAABgCokHAAB0oPftdKu1NusaoDtVdXRrbems6wDohf9uAkatYN0cPesCADrjv5twN6fxAAAARqfxAAAARqfxgHVjThlg7fjvJtzNWVwOAACMTuIBAACMTuMBa6GqTqqqa6rqglnXAtCLqnpGVf17Vf1HVR0363qA2dB4wNo5OckzZl0EQC+qarMkb0lycJI9kxxeVXvOtipgFjQesBZaa59Lcv2s6wDoyL5J/qO19u3W2k+TvDvJoTOuCZgBjQcAMKbdknx/zveXDceAuxmNBwAAMDqNBwAwpsuTPHDO9w8YjgF3MxoPAGBMX0nysKp6UFVtkeQFST4w45qAGdB4wFqoqtOTfDHJw6vqsqo6atY1AWzMWmsrkhyT5ONJvpnkPa21C2dbFTALPrkcAAAYncQDAAAYncYDAAAYncYDAAAYncYDAN8fBtcAAAH2SURBVAAYncYDAAAYncYDYCNUVc+uqlZVjxi+P6CqPrSa6w6oqies53vdtD6vB4CF0HgAbJwOT3L28HU+ByRZr8YDABaDxgNgI1NVWyd5UpKjMvmU51XP/2JV/VtVPSTJbyf5/ao6r6r2r6qTq+q5c669aeU9q+qsqjq3qs6vqkMX6ccBgCTJ5rMuAID/4tAkH2utXVxV11XVY1eeGMaq/i7Joa21S6vq75Pc1Fo7YTh/1BrueUuSw1prN1bVTkm+VFUfaD5FFoBFIvEA2PgcnuTdw/N35z/HrR6ZZGmSX2mtXbqW96wkf1FVX0/yqSS7JdllA9QKAAsi8QDYiFTVjkmemuRRVdWSbJakJflwkiuT3CvJY5JcsYZbrMjwj0pVtSTJFsPxFyXZOcljW2u3VdV3h3sBwKKQeABsXJ6b5NTW2s+01vZorT0wyXeS7J/khiS/nOR/V9UBw/XLk2wz5/XfTbJyNOtZSe4xPN8uyTVD03Fgkp8Z9acAgFVoPAA2LocnOXOVY2cMx9NauzrJIUneUlX7JflgksNWLi5P8rYkT6mqryV5fJKbh3u8M8k+VXV+kpcmuWj0nwQA5ijrCgEAgLFJPAAAgNFpPAAAgNFpPAAAgNFpPAAAgNFpPAAAgNFpPAAAgNFpPAAAgNFpPAAAgNH9fy9BXJj3s1dYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Export"
      ],
      "metadata": {
        "id": "1Dt_0h18nH6f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Test"
      ],
      "metadata": {
        "id": "h7xXzWP5nxM-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label data test dengan hasil prediksi pada masing-masing model"
      ],
      "metadata": {
        "id": "NHcUK-Np7amh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_microarray_prediksi = y_test1.to_frame()\n",
        "test_microarray_prediksi['RF1'] = rf1_prediksi.astype('int')\n",
        "test_microarray_prediksi['RF2'] = rf2_prediksi.astype('int')\n",
        "test_microarray_prediksi['RF3'] = rf3_prediksi.astype('int')\n",
        "test_microarray_prediksi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VML7QD3j1bw_",
        "outputId": "2308256f-ba58-4e82-d4bb-470c574aaa43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Label  RF1  RF2  RF3\n",
              "0       0    1    1    1\n",
              "1       1    1    1    1\n",
              "2       1    0    0    0\n",
              "3       1    1    1    1\n",
              "4       0    0    0    1\n",
              "5       0    0    0    0\n",
              "6       1    1    1    1\n",
              "7       1    1    1    1\n",
              "8       1    1    1    1\n",
              "9       1    1    1    1\n",
              "10      1    1    1    1\n",
              "11      1    0    0    0\n",
              "12      0    0    0    0\n",
              "13      0    1    1    1\n",
              "14      0    0    0    0\n",
              "15      1    1    1    1\n",
              "16      1    1    1    1\n",
              "17      0    1    1    1\n",
              "18      0    0    0    0\n",
              "19      0    1    1    1\n",
              "20      1    1    1    1\n",
              "21      1    0    0    1\n",
              "22      1    1    1    1\n",
              "23      0    0    0    0\n",
              "24      0    0    0    0\n",
              "25      1    1    1    1\n",
              "26      1    1    1    1\n",
              "27      0    0    0    0\n",
              "28      1    1    0    1\n",
              "29      1    1    1    1\n",
              "30      1    1    1    1\n",
              "31      0    1    1    1\n",
              "32      1    1    1    1\n",
              "33      0    1    1    0\n",
              "34      0    0    0    0\n",
              "35      1    1    1    1\n",
              "36      0    0    0    0\n",
              "37      1    1    1    1\n",
              "38      0    0    0    0\n",
              "39      1    1    1    1\n",
              "40      0    0    0    0\n",
              "41      1    0    0    0\n",
              "42      1    1    1    1\n",
              "43      1    1    1    1\n",
              "44      1    1    1    1\n",
              "45      0    0    0    0\n",
              "46      0    0    0    0\n",
              "47      1    1    1    1\n",
              "48      1    0    0    1\n",
              "49      0    1    1    1\n",
              "50      0    0    0    0\n",
              "51      1    1    1    1\n",
              "52      0    1    1    1\n",
              "53      0    0    0    0\n",
              "54      0    1    1    1\n",
              "55      0    1    1    1\n",
              "56      0    0    0    0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d60d2739-c4c5-46f3-87ae-bdb5cce35f42\">\n",
              "    <div 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>Label</th>\n",
              "      <th>RF1</th>\n",
              "      <th>RF2</th>\n",
              "      <th>RF3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d60d2739-c4c5-46f3-87ae-bdb5cce35f42')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d60d2739-c4c5-46f3-87ae-bdb5cce35f42 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-d60d2739-c4c5-46f3-87ae-bdb5cce35f42');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Export ke CSV"
      ],
      "metadata": {
        "id": "k001VpPw7eEW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_microarray_prediksi.to_csv(r'test_microarray_prediksi_rf.csv', index=False, header=True)"
      ],
      "metadata": {
        "id": "N7oJ6hS8ng4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Suspect"
      ],
      "metadata": {
        "id": "b26CrNofoZ0a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit model"
      ],
      "metadata": {
        "id": "KAmK8V9n7iXM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_model.fit(X_train1, y_train1)\n",
        "rf2_model.fit(X_train2, y_train2)\n",
        "rf3_model.fit(X_train3, y_train3)"
      ],
      "metadata": {
        "id": "lsrv4n2cpB4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b87c6320-a985-45ba-8eda-cee8f94dc4e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediksi dengan Suspect"
      ],
      "metadata": {
        "id": "TEjJDtp17j14"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_prediksi_suspect = rf1_model.predict(df_suspect1).astype('int')\n",
        "rf2_prediksi_suspect = rf2_model.predict(df_suspect2).astype('int')\n",
        "rf3_prediksi_suspect = rf3_model.predict(df_suspect3).astype('int')"
      ],
      "metadata": {
        "id": "FQ-5K7jCo3wE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melihat hasil prediksi masing-masing model"
      ],
      "metadata": {
        "id": "i_qJGwFv7lv6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "calon_dataframe = {'RF1' : rf1_prediksi_suspect,\n",
        "                   'RF2' : rf2_prediksi_suspect,\n",
        "                   'RF3' : rf3_prediksi_suspect}\n",
        "suspect_prediksi = pd.DataFrame(calon_dataframe)\n",
        "suspect_prediksi"
      ],
      "metadata": {
        "id": "hdkYKajapY6u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d107620-ba4d-49a3-cef8-7428ce789a65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RF1  RF2  RF3\n",
              "0    0    1    1\n",
              "1    1    1    1\n",
              "2    1    1    1\n",
              "3    1    1    1\n",
              "4    0    1    1"
            ],
            "text/html": [
              "\n",
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              "      <th>RF1</th>\n",
              "      <th>RF2</th>\n",
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              "    }\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-78aa4c65-090d-4736-b994-2a1e372d6757 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-78aa4c65-090d-4736-b994-2a1e372d6757');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Export ke CSV"
      ],
      "metadata": {
        "id": "hZn3KE5w7pQ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "suspect_prediksi.to_csv(r'suspect_prediksi_rf.csv', index=False, header=True)"
      ],
      "metadata": {
        "id": "f6_LOhm7pq9g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}