AHP_Kelompok_8.ipynb

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      "cell_type": "markdown",
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      "source": [
        "## PROGRAM MADM AHP (ANALYTICAL HIERACHY PROCESS) DALAM BAHASA PYTHON"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a956542",
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      },
      "source": [
        "### Deskripsi Masalah"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a18365eb",
      "metadata": {
        "id": "a18365eb"
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      "source": [
        "Manajer pembelian Suroboyo Bus bertanggung jawab untuk memutuskan perusahaan mana yang tersedia untuk membeli 50 bus untuk rute baru. Setelah studi kelayakan awal dan analisis merek, model beberapa bus, dan proses yang terkait dengan pembelian, ia mempersempit daftar alternatif keputusannya menjadi tiga opsi;\n",
        "1. **Proyek 1**: Pembelian model Eksekutif,\n",
        "2. **Proyek 2**: Pembelian model Medium, dan\n",
        "3. **Proyek 3**: Pembelian model Ekonomi.\n",
        "\n",
        "Suroboyo Bus memutuskan bahwa kriteria berikut relevan untuk proses keputusan pembeliannya:\n",
        "1. **Mil per galon (MPG)**\n",
        "2. **Kenyamanan**\n",
        "3. **Gaya**\n",
        "4. **Harga**"
      ]
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    {
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      "source": [
        "### Mengimport libraries"
      ]
    },
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      "id": "d09c814c",
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
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      "id": "d02be9ed",
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      },
      "source": [
        "### Tabel 1: Penilaian Verbal ke Peringkat Numerik"
      ]
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              "                                                  Peringkat Numerik\n",
              "Tingkat Kepentingan                                                \n",
              "Mutlak lebih penting                                              9\n",
              "Sangat lebih penting hingga mutlak lebih penting                  8\n",
              "Sangat lebih penting                                              7\n",
              "Lebih penting hingga sangat lebih penting                         6\n",
              "Lebih penting                                                     5\n",
              "Cukup penting hingga lebih penting                                4\n",
              "Cukup penting                                                     3\n",
              "Sama penting hingga cukup penting                                 2\n",
              "Sama penting                                                      1"
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              "      <th>Peringkat Numerik</th>\n",
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              "      <th>Tingkat Kepentingan</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mutlak lebih penting</th>\n",
              "      <td>9</td>\n",
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              "      <th>Sangat lebih penting hingga mutlak lebih penting</th>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>Sangat lebih penting</th>\n",
              "      <td>7</td>\n",
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              "      <th>Lebih penting hingga sangat lebih penting</th>\n",
              "      <td>6</td>\n",
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              "      <th>Lebih penting</th>\n",
              "      <td>5</td>\n",
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              "      <th>Cukup penting hingga lebih penting</th>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>Cukup penting</th>\n",
              "      <td>3</td>\n",
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              "      <th>Sama penting hingga cukup penting</th>\n",
              "      <td>2</td>\n",
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      "source": [
        "Rating_dic = {'Tingkat Kepentingan': ['Mutlak lebih penting','Sangat lebih penting hingga mutlak lebih penting',\n",
        "                                       'Sangat lebih penting','Lebih penting hingga sangat lebih penting',\n",
        "                                      'Lebih penting','Cukup penting hingga lebih penting',\n",
        "                                     'Cukup penting', 'Sama penting hingga cukup penting','Sama penting' ],\n",
        "                 'Peringkat Numerik': [9,8,7,6,5,4,3,2,1]}\n",
        "\n",
        "Rating_comparison = pd.DataFrame(Rating_dic).set_index('Tingkat Kepentingan')\n",
        "Rating_comparison"
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      "source": [
        "### Tabel 2: Perbandingan Berpasangan Empat Kriteria Soal Pemilihan Bus"
      ]
    },
    {
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              "                         Kriteria Terpenting  \\\n",
              "Perbandingan Berpasangan                       \n",
              "Harga-MPG                              Harga   \n",
              "Harga-Kenyamanan                       Harga   \n",
              "Harga-Gaya                             Harga   \n",
              "MPG-Kenyamanan                    Kenyamanan   \n",
              "MPG-Gaya                                Gaya   \n",
              "Kenyamanan-Gaya                         Gaya   \n",
              "\n",
              "                                            Seberapa Penting  \\\n",
              "Perbandingan Berpasangan                                       \n",
              "Harga-MPG                                      Cukup penting   \n",
              "Harga-Kenyamanan           Sama penting hingga cukup penting   \n",
              "Harga-Gaya                 Sama penting hingga cukup penting   \n",
              "MPG-Kenyamanan            Cukup penting hingga lebih penting   \n",
              "MPG-Gaya                  Cukup penting hingga lebih penting   \n",
              "Kenyamanan-Gaya            Sama penting hingga cukup penting   \n",
              "\n",
              "                          Peringkat Numerik  \n",
              "Perbandingan Berpasangan                     \n",
              "Harga-MPG                                 3  \n",
              "Harga-Kenyamanan                          2  \n",
              "Harga-Gaya                                2  \n",
              "MPG-Kenyamanan                            4  \n",
              "MPG-Gaya                                  4  \n",
              "Kenyamanan-Gaya                           2  "
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              "      <td>3</td>\n",
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        "comparison_dic = {'Perbandingan Berpasangan': ['Harga-MPG','Harga-Kenyamanan','Harga-Gaya','MPG-Kenyamanan','MPG-Gaya','Kenyamanan-Gaya'],\n",
        "                 'Kriteria Terpenting': ['Harga','Harga','Harga','Kenyamanan','Gaya','Gaya'],\n",
        "                 'Seberapa Penting': ['Cukup penting','Sama penting hingga cukup penting','Sama penting hingga cukup penting','Cukup penting hingga lebih penting','Cukup penting hingga lebih penting','Sama penting hingga cukup penting'],\n",
        "                 'Peringkat Numerik': [3,2,2,4,4,2]}\n",
        "\n",
        "criteria_comparison = pd.DataFrame(comparison_dic).set_index('Perbandingan Berpasangan')\n",
        "criteria_comparison"
      ]
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    {
      "cell_type": "markdown",
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      "metadata": {
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      },
      "source": [
        "**Konversi Penilaian Verbal ke Peringkat Numerik**"
      ]
    },
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              "                             Harga  Kenyamanan  Gaya  MPG\n",
              "Perbandingan Berpasangan                                 \n",
              "Harga                     1.000000        2.00  2.00    3\n",
              "Kenyamanan                0.500000        1.00  0.50    4\n",
              "Gaya                      0.500000        2.00  1.00    4\n",
              "MPG                       0.333333        0.25  0.25    1"
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              "\n",
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              "    <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>Harga</th>\n",
              "      <th>Kenyamanan</th>\n",
              "      <th>Gaya</th>\n",
              "      <th>MPG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perbandingan Berpasangan</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Harga</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kenyamanan</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gaya</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87a36b5a-6e93-486a-8929-fa25e4ba52df')\"\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-87a36b5a-6e93-486a-8929-fa25e4ba52df 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-87a36b5a-6e93-486a-8929-fa25e4ba52df');\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": 4
        }
      ],
      "source": [
        "Numeric_dic = {'Perbandingan Berpasangan': ['Harga','Kenyamanan','Gaya','MPG'],\n",
        "                 'Harga': [1,1/2,1/2,1/3],\n",
        "                 'Kenyamanan': [2,1,2,1/4],\n",
        "                 'Gaya': [2,1/2,1,1/4],\n",
        "                 'MPG': [3,4,4,1]}\n",
        "\n",
        "Numeric_comparison = pd.DataFrame(Numeric_dic).set_index('Perbandingan Berpasangan')\n",
        "Numeric_comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "121ba250",
      "metadata": {
        "id": "121ba250"
      },
      "source": [
        "#### Menghitung bobot untuk setiap kriteria"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "5ff28830",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5ff28830",
        "outputId": "c62a0a8f-1c64-4c4d-9d08-e6a0755658ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             Harga  Kenyamanan      Gaya       MPG\n",
              "Perbandingan Berpasangan                                          \n",
              "Harga                     0.428571    0.380952  0.533333  0.250000\n",
              "Kenyamanan                0.214286    0.190476  0.133333  0.333333\n",
              "Gaya                      0.214286    0.380952  0.266667  0.333333\n",
              "MPG                       0.142857    0.047619  0.066667  0.083333"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-735a7e49-8b4e-4024-8b13-1548c8fe922c\">\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>Harga</th>\n",
              "      <th>Kenyamanan</th>\n",
              "      <th>Gaya</th>\n",
              "      <th>MPG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perbandingan Berpasangan</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Harga</th>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.380952</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kenyamanan</th>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.190476</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gaya</th>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.380952</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.047619</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.083333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-735a7e49-8b4e-4024-8b13-1548c8fe922c')\"\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-735a7e49-8b4e-4024-8b13-1548c8fe922c 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-735a7e49-8b4e-4024-8b13-1548c8fe922c');\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": 5
        }
      ],
      "source": [
        "criteria_weight = Numeric_comparison.copy()\n",
        "for col in ['Harga','Kenyamanan','Gaya','MPG']:\n",
        "    criteria_weight[col] = criteria_weight[col] / criteria_weight[col].sum()\n",
        "criteria_weight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "86cd7fc4",
      "metadata": {
        "id": "86cd7fc4"
      },
      "outputs": [],
      "source": [
        "Price_weight = (criteria_weight['Harga'][0] + criteria_weight['Kenyamanan'][0] + criteria_weight['Gaya'][0] +criteria_weight['MPG'][0])/4\n",
        "Comfort_weight = (criteria_weight['Harga'][1] + criteria_weight['Kenyamanan'][1] + criteria_weight['Gaya'][1] +criteria_weight['MPG'][1])/4\n",
        "Style_weight = (criteria_weight['Harga'][2] + criteria_weight['Kenyamanan'][2] + criteria_weight['Gaya'][2] +criteria_weight['MPG'][2])/4\n",
        "MPG_weight = (criteria_weight['Harga'][3] + criteria_weight['Kenyamanan'][3] + criteria_weight['Gaya'][3] +criteria_weight['MPG'][3])/4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "e2578656",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2578656",
        "outputId": "55f86ec7-835e-4c76-b595-c75ac3a55696"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bobot Harga : 0.398\n",
            "Bobot Kenyamanan : 0.218\n",
            "Bobot Gaya : 0.299\n",
            "Bobot MPG : 0.085\n"
          ]
        }
      ],
      "source": [
        "print(f'Bobot Harga : {Price_weight:.3f}\\nBobot Kenyamanan : {Comfort_weight:.3f}\\nBobot Gaya : {Style_weight:.3f}\\nBobot MPG : {MPG_weight:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "386b208d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "386b208d",
        "outputId": "5da55a0f-ecd9-443a-a453-ba38c1b1d45a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Bobot Kriteria')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BRURE7400m+v1wPWSLhqaYTUiIrYcoy4YBFwgKQsGRURsYbJgUERENGp1waCIiNh0jXibq+2ngD3LXEoREbEFaXPBoIiI2IR1kyCWlMfQgkEAWXshnu0yjV5nc/aO/ErE5meku5j2sX3r0IJBHcf+rN2wIiKi10Yag/iqpN/tLJR0NvCe1iKKiIiNwkgJ4miqdRpeC6DK56mWBT1gDGKLiIgeGjZB2F4AHAlcKmkacCXQB0yz/fAYxRcRET0ybIKQtAuwHDgBuBR4Engv8LxyLCIiNmMjXWJaAAwCFwOPAPsDN9XKRyVpmqS7JC2WdGbD8fdJWiTpNknfkbRn7dgJku4ujxPW5kVFRMT6G+mb1JPX58TlW9izgIOoeiLzJQ10rC19M9Bv+7FyZ9T5wNtKD+XDQD/VLbULStsH1yemiIjoXjcLBq2r/YDFtpfafgKYC0yvV7D9PduPld0bgd3K9iHAtbYfKEnhWmBai7FGRESHNhPERGBZbX95KRvOScA316atpBmSBiUNrly5cj3DjYiIujYTRNckHU91Oenv16ad7dm2+2339/X1tRNcRMQWatQEIemSbsoarAB2r+3vVso6z3Ug8AHgCNur1qZtRES0p5sexN71nTL4vMY3rBvMB6ZImlxmgz0WGOg4177AhVTJ4b7aoWuAgyXtLGln4OBSFhERY2Sk70GcJekR4JWSHpb0SNm/D/j30U5sezVwKtUf9juAy20vlDRT0tBqdH9Pte71FZJuKTPGYvsB4CNUSWY+MLOURUTEGBnpNtdzgXMlnWv7rHU5ue15wLyOsg/Vtg8coe0cYM66PG9ERKy/Uaf7tn1W+cT/xlJ0ne2vtxtWRET0WjeD1OcCpwOLyuN0SR9vO7CIiOitbhYMegsw1fbTAJK+SPUN6Pe3GVhERPRWt9+D2Km2vWMbgURExMalmx7EucDNkr4HiGosYo2J9yIiYvPSzSD1v0m6Dng11cR5Z9j+eduBRUREb3XTgwB4LfAGqgQxHvhqaxFFRMRGoZu7mD4LnAzcDvwYeK+kWW0HFhERvdVND+JNwMtsG565i2lhq1FFRETPdXMX02Jgj9r+7qUsIiI2Y8P2ICR9jWrMYXvgDkk3lUP7US09GhERm7GRLjF9YsyiiIiIjc5Ik/VdP7Qt6QVUt7kC3NQxNXdERGyGurmL6RiqS0pHA8cAP5R0VNuBRUREb3VzF9MHgFcP9Rok9QH/AVzZZmAREdFb3dzF9JyOS0r3d9kuIiI2Yd30IL4l6Rrg38r+2+hYBCgiIjY/o/YEbP811brRryyP2bbP6ObkkqZJukvSYklrTPAn6Y2SfiRpdee4hqSnyjKkzyxFGhERY6eruZhsfwX4iqRdqS4xjUrSOGAWcBCwHJgvacD2olq1nwInAn/VcIrHbU/t5rkiImLDG7YHIek1kq6T9BVJ+0r6MdVcTL+QNK2Lc+8HLLa91PYTwFxger2C7Z/Yvg14ej1eQ0REtGCkS0wXAB+nGnv4LvBu279JtR7EuV2ceyKwrLa/vJR1a2tJg5JulHRkUwVJM0qdwZUrV67FqSMiYjQjJYjxtr9t+wrg57ZvBLB959iExp62+4F3AJ+W9KLOCrZn2+633d/X1zdGYUVEbBlGShD1yz6PdxxzF+deQTWx35DdSllXbK8o/y4FrgP27bZtRESsv5EGqfeR9DDVMqPblG3K/tZdnHs+MEXSZKrEcCxVb2BUknYGHrO9qgyMvx44v5u2ERGxYYw0F9O49Tmx7dWSTgWuAcYBc2wvlDQTGLQ9IOnVVKvT7QwcLukc23sDLwMulPQ0VS/nvI67nyIiomXdLjm6TmzPo+NLdbY/VNueT3XpqbPdD4BXtBlbRESMLFNmREREoySIiIholAQRERGNkiAiIqJREkRERDRKgoiIiEZJEBER0SgJIiIiGiVBREREoySIiIholAQRERGNkiAiIqJREkRERDRKgoiIiEZJEBER0SgJIiIiGiVBREREo1YThKRpku6StFjSmQ3H3yjpR5JWSzqq49gJku4ujxPajDMiItbUWoKQNA6YBRwK7AW8XdJeHdV+CpwIXNbRdhfgw8D+wH7AhyXt3FasERGxpjZ7EPsBi20vtf0EMBeYXq9g+ye2bwOe7mh7CHCt7QdsPwhcC0xrMdaIiOjQZoKYCCyr7S8vZRusraQZkgYlDa5cuXKdA42IiDVt0oPUtmfb7rfd39fX1+twIiI2K20miBXA7rX93UpZ220jImIDaDNBzAemSJosaQJwLDDQZdtrgIMl7VwGpw8uZRERMUZaSxC2VwOnUv1hvwO43PZCSTMlHQEg6dWSlgNHAxdKWljaPgB8hCrJzAdmlrKIiBgj49s8ue15wLyOsg/VtudTXT5qajsHmNNmfBERMbxNepA6IiLakwQRERGNWr3EFBExFqReR9BbdjvnTQ8iIiIaJUFERESjJIiIiGiUBBEREY2SICIiolESRERENEqCiIiIRkkQERHRKAkiIiIaJUFERESjJIiIiGiUBBEREY2SICIiolESRERENGo1QUiaJukuSYslndlwfCtJXy7HfyhpUimfJOlxSbeUx+fbjDMiItbU2noQksYBs4CDgOXAfEkDthfVqp0EPGj7xZKOBf4OeFs5tsT21Lbii4iIkbXZg9gPWGx7qe0ngLnA9I4604Evlu0rgTdLW/rSHxERG4c2E8REYFltf3kpa6xjezXwEPD8cmyypJslXS/p95qeQNIMSYOSBleuXLlho4+I2MJtrIPUPwP2sL0v8D7gMkk7dFayPdt2v+3+vr6+MQ8yImJz1maCWAHsXtvfrZQ11pE0HtgRuN/2Ktv3A9heACwBXtJirBER0aHNBDEfmCJpsqQJwLHAQEedAeCEsn0U8F3bltRXBrmR9EJgCrC0xVgjIqJDa3cx2V4t6VTgGmAcMMf2QkkzgUHbA8C/AJdIWgw8QJVEAN4IzJT0JPA0cLLtB9qKNSIi1tRaggCwPQ+Y11H2odr2fwNHN7S7CriqzdgiImJkG+sgdURE9FgSRERENEqCiIiIRkkQERHRKAkiIiIaJUFERESjJIiIiGiUBBEREY2SICIiolESRERENEqCiIiIRkkQERHRKAkiIiIaJUFERESjJIiIiGiUBBEREY2SICIiolGrCULSNEl3SVos6cyG41tJ+nI5/kNJk2rHzirld0k6pM04IyJiTa0lCEnjgFnAocBewNsl7dVR7STgQdsvBj4F/F1puxfV+tR7A9OAz5bzRUTEGGmzB7EfsNj2UttPAHOB6R11pgNfLNtXAm+WpFI+1/Yq2/cAi8v5IiJijIxv8dwTgWW1/eXA/sPVsb1a0kPA80v5jR1tJ3Y+gaQZwIyy+6ikuzZM6D2xK/DLnj271LOn3kB6+/4dl/dvfejsvH/rYz1/ffcc7kCbCaJ1tmcDs3sdx4YgadB2f6/j2FTl/Vs/ef/Wz+b6/rV5iWkFsHttf7dS1lhH0nhgR+D+LttGRESL2kwQ84EpkiZLmkA16DzQUWcAOKFsHwV817ZL+bHlLqfJwBTgphZjjYiIDq1dYipjCqcC1wDjgDm2F0qaCQzaHgD+BbhE0mLgAaokQql3ObAIWA2cYvuptmLdSGwWl8p6KO/f+sn7t342y/dP1Qf2iIiIZ8s3qSMiolESRERENEqC2MAkPdqxf6KkC3oVz1ipv25Jh0n6/5KGvb862iXpBZIuk7RU0gJJ/yXpD3sd16ZGkiVdWtsfL2mlpK+X/RPL/i2SFkl6T63uNEk3SbqzHP+ypD168TrW1Sb9PYjNkaTxtlf3Oo51JenNwD8Bh9i+t9fxbInKbARXA1+0/Y5StidwRE8D2zT9Gni5pG1sPw4cxJq33H/Z9qmSfgNYKGkA6AM+Axxh+w4ASUcAk4Cfjln06yk9iDEk6fAyKeHNkv5D0gtK+dmSLpF0A9VdXX2SrpW0UNIXJN0raddS9+ryiXBh+Sb5RkPSG4F/Bt5qe0kpO758irpF0oVDc2pJelTSxyTdKunG8ol3e0n3SHpuqbPD0L6k90iaX+pfJWnbUuciSZ8r51gq6QBJcyTdIemiWmyfkzRY3rdzauU/kXSOpB9Jul3S75Ty/cqn7psl/UDSS0v5iZK+Iulbku6WdP4Yvb1r403AE7Y/P1Rg+17bn5E0SdL/K6/3R5JeByDpYklHDtWX9CVJ04erv4WZB7ylbL8d+LemSrbvA5ZQfTP5DODjQ8mhHB+w/Z8tx7ph2c5jAz6Ap4Bbao+fAheUYzvzv3eOvRv4h7J9NrAA2KbsXwCcVbanAQZ2Lfu7lH+3AX4MPL/Xr7nE8yTVrcqvrJW9DPga8Nyy/1ngT8q2gcPL9vnAB8v2vwJHlu0Ztffo+bXzfhT4P2X7Iqp5vobm8HoYeAXVh58FwNSO920ccN1QnMBPauf6c+ALZXsHYHzZPhC4qmyfCCyl+lLn1sC9wO69fv87fhanAZ8a5ti2wNZlewrVLecAvw9cXbZ3BO6husLQWH9LeQCPAq+kmitu6/I7fQDw9dr/h6Hf7xcC9wG7AD8C9ul1/Ov7yCWmDe9x21OHdiSdCAx9BX834MuSfguYQPVLOGTAVRcW4A3AHwLY/pakB2v1TqtdS96d6pf2/g3+Ktbek8APqGboPb2UvRn4XWB+ddWDbah+gQCeAL5ethdQdd0BvgD8DdUlkncBQ9d0Xy7po8BOwHZU368Z8jXblnQ78AvbtwNIWkjVpb8FOKb0uMYDv0U1w/Btpf1XanH8UdneEfiipClUyey5tef7ju2HynMsovrEWJ93bKMiaRbV/6knqJLdBZKmUn2YeQmA7eslfVZSH/DHVAlxtaTnNdXfkti+TdVSBG+n6k10epukNwCrgPfafkC1yZEkPR/4DlWynW37E60HvYHkEtPY+gzVp41XAO+l+kQy5NejNZZ0ANUv+Gtt7wPc3HGOXnoaOAbYT9L7S5moroNPLY+X2j67HHvS5WMX1R+e8QC2bwAmldc6zvaPS52LgFPLe3cOz37dq2oxrKqVPw2MV/Vt/L8C3mz7lcA3hmn/TBzAR4Dv2X45cPgw9TvbbCwWAq8a2rF9ClWy7gP+L/ALYB+qDy4Tau0uBo6nSsxzStlI9bckA8AnaL689OXy/3t/218tZc/8DGzfXz40zqb6cLPJSIIYWzvyvwNcJ4xQ7waqP7ZIOpjq0tRQ+wdtP1aulb+mrUDXhe3HqK7VHifpJKpPTUeVwTsk7aLu7my6GLiM6nLTkO2Bn5XxiePWMrQdqBLwQ2Xc59Au2tR/Vieu5fP12neBrSX9Wa1s2/LvjsDPbD8NvJPqktuQi4C/ALC9qIv6W5I5wDlDvdMunA98QNLLamXbDld5Y5UEMbbOBq6QtICRpwY+BzhY0o+Bo4GfA48A36L6RHwHcB7PnhJ9o2D7Aapxkw8CLy7/flvSbcC1VJd3RvMlqqRY/7T2t8APqZLnnWsZ061Uva07qRLPDV00Ox84V9LNbHw9hBGVntmRwO+XQf6bqNZdOYNqHOgESbcCv0Ot52r7F8AdPDsxD1t/S2J7ue1/Wov6t1Ndar1Y1aqYN1CNyV3WVoxtyFQbGyFJWwFPlWvArwU+Vx/X2NxJOgqYbvudvY5lS1LuDLsdeNXQGEts2TapT0ZbkD2AyyU9h2pg8T2j1N9sSPoM1SWgw3ody5ZE0oFUk2d+KskhhqQHERERjTIGERERjZIgIiKiURJEREQ0SoKIGIW6nKlW0hGSzizbR0raax2e65lzRPRaBqkjRiHpUdvbqZqp9kKqmWqXdNR51iy8ZaLAr9u+ci2eZ5OeyTc2P0kQEaMoPYjDqL5pfJjtO0v5RcB/A/tSffnuNqrpKC6jmmfqofL443KqWVTTXTwGvMf2ncOdw9X00YdTfdFwAtV8W8eVL7NFjIl8DyJidFtRTR54wFByqNkNeJ3tp8rEjNj+gao1AZ7pQUj6DnCy7bsl7U/1DeU3DXeO4vvAa8pEhO+mmsTwL9t5iRFrSoKIGF3TTLVDrrD91EiNJW0HvI5qmpWh4q26OMdIs/9GtC6D1BGja5qpdkg3cxM9B/hVbVbbqbbrk7gNd46RZv+NaF0SREQXGmaqHc0jVDPQYtCd+J8AAABxSURBVPth4B5JR0O1JKikfbo4R7ez/0a0Igkiokv1mWpVrS88krnAX5clS19ENUX5SWVW1IVUq9+N5my6m/03ohW5iykiIhqlBxEREY2SICIiolESRERENEqCiIiIRkkQERHRKAkiIiIaJUFERESj/wFsR+UmqgqPxAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.bar(['Harga','Kenyamanan','Gaya','MPG'],[Price_weight, Comfort_weight, Style_weight, MPG_weight], color = ['red', 'orange', 'Green', 'blue'])\n",
        "plt.title('Perbandingan Kriteria')\n",
        "plt.xlabel('Kriteria')\n",
        "plt.ylabel('Bobot Kriteria')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dddf6dd5",
      "metadata": {
        "id": "dddf6dd5"
      },
      "source": [
        "Hasil bobot di atas menunjukkan bahwa kriteria Harga merupakan kriteria yang paling penting dari semua kriteria, dengan bobot 0.398, disusul kriteria Gaya dengan bobot 0.299, disusul kriteria Kenyamanan dengan bobot 0.218, dan kriteria MPG dengan bobot 0.085"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac8b8f36",
      "metadata": {
        "id": "ac8b8f36"
      },
      "source": [
        "### Tabel 3: Perbandingan Berpasangan yang menunjukkan Preferensi untuk proyek berdasarkan kriteria"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6608ef61",
      "metadata": {
        "id": "6608ef61"
      },
      "source": [
        "### Kriteria Harga"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "e32cfb21",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "e32cfb21",
        "outputId": "69aff47a-8b14-4979-db5f-7285effeda79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Proyek 1  Proyek 2  Proyek 3\n",
              "HARGA                                 \n",
              "Proyek 1         1  0.333333      0.25\n",
              "Proyek 2         3  1.000000      0.50\n",
              "Proyek 3         4  2.000000      1.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d512ddfd-6e0b-41ec-9fe4-4b01d3b6a244\">\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>Proyek 1</th>\n",
              "      <th>Proyek 2</th>\n",
              "      <th>Proyek 3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HARGA</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Proyek 1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Proyek 2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Proyek 3</th>\n",
              "      <td>4</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d512ddfd-6e0b-41ec-9fe4-4b01d3b6a244')\"\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-d512ddfd-6e0b-41ec-9fe4-4b01d3b6a244 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-d512ddfd-6e0b-41ec-9fe4-4b01d3b6a244');\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": 20
        }
      ],
      "source": [
        "PRICE_dic = {'HARGA': ['Proyek 1','Proyek 2','Proyek 3'],\n",
        "                 'Proyek 1': [1,3,4],\n",
        "                 'Proyek 2': [1/3,1,2],\n",
        "                 'Proyek 3': [1/4,1/2,1,]}\n",
        "\n",
        "PRICE_comparison = pd.DataFrame(PRICE_dic).set_index('HARGA')\n",
        "PRICE_comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "c8150685",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "c8150685",
        "outputId": "249d1e12-3040-4a12-e506-722bd53db21a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Proyek 1  Proyek 2  Proyek 3\n",
              "HARGA                                 \n",
              "Proyek 1     0.125       0.1  0.142857\n",
              "Proyek 2     0.375       0.3  0.285714\n",
              "Proyek 3     0.500       0.6  0.571429"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Proyek 1</th>\n",
              "      <th>Proyek 2</th>\n",
              "      <th>Proyek 3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HARGA</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Proyek 1</th>\n",
              "      <td>0.125</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Proyek 2</th>\n",
              "      <td>0.375</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Proyek 3</th>\n",
              "      <td>0.500</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.571429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3865d5fe-8b6a-40b3-ad3b-c3e6cb768e98 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-3865d5fe-8b6a-40b3-ad3b-c3e6cb768e98');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "PRICE_criteria = PRICE_comparison.copy()\n",
        "for col in ['Proyek 1','Proyek 2','Proyek 3']:\n",
        "    PRICE_criteria[col] = PRICE_criteria[col] / PRICE_criteria[col].sum()\n",
        "PRICE_criteria"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "355bd0ed",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "355bd0ed",
        "outputId": "507573d5-ba06-4100-9494-d2e660c1d4ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bobot Proyek Berdasarkan Kriteria Harga\n",
            "--------------------------------------\n",
            "Proyek 1 : 0.123\n",
            "Proyek 2: 0.320\n",
            "Proyek 3 : 0.557\n"
          ]
        }
      ],
      "source": [
        "PRICE_P1 = (PRICE_criteria['Proyek 1'][0] + PRICE_criteria['Proyek 2'][0] + PRICE_criteria['Proyek 3'][0])/3\n",
        "PRICE_P2 = (PRICE_criteria['Proyek 1'][1] + PRICE_criteria['Proyek 2'][1] + PRICE_criteria['Proyek 3'][1])/3\n",
        "PRICE_P3 = (PRICE_criteria['Proyek 1'][2] + PRICE_criteria['Proyek 2'][2] + PRICE_criteria['Proyek 3'][2])/3\n",
        "\n",
        "print(f'Bobot Proyek Berdasarkan Kriteria Harga\\n--------------------------------------\\nProyek 1 : {PRICE_P1:.3f}\\nProyek 2: {PRICE_P2:.3f}\\nProyek 3 : {PRICE_P3:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "e027318a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "e027318a",
        "outputId": "a814d342-6298-4950-8d13-3872c500544f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Bobot Proyek')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.bar(['Proyek 1','Proyek 2','Proyek 3'],[PRICE_P1,PRICE_P2,PRICE_P3], color = ['orange', 'green', 'blue'])\n",
        "plt.title('Bobot Proyek Berdasarkan Kriteria Harga')\n",
        "plt.ylabel('Bobot Proyek')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "415b45a4",
      "metadata": {
        "id": "415b45a4"
      },
      "source": [
        "Hasil di atas menunjukkan bahwa Proyek 3, pembelian model Ekonomi, memiliki bobot tertinggi 0.557. Ini berarti jika 3 proyek dianalisis berdasarkan kriteria Harga saja, Proyek 3 akan menjadi kandidat terbaik untuk diseleksi."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f2aa13b",
      "metadata": {
        "id": "6f2aa13b"
      },
      "source": [
        "### Kriteria MPG "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "6749e09e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6749e09e",
        "outputId": "4537d675-431a-4630-cddd-d823ddb859d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bobot Proyek Berdasarkan Kriteria MPG\n",
            "--------------------------------------\n",
            "Proyek 1 : 0.087\n",
            "Proyek 2 : 0.274\n",
            "Proyek 3 : 0.639\n"
          ]
        }
      ],
      "source": [
        "MPG_dic = {'MPG': ['Proyek 1','Proyek 2','Proyek 3'],\n",
        "                 'Proyek 1': [1,4,6],\n",
        "                 'Proyek 2': [1/4,1,3],\n",
        "                 'Proyek 3': [1/6,1/3,1,]}\n",
        "\n",
        "MPG_comparison = pd.DataFrame(MPG_dic).set_index('MPG')\n",
        "\n",
        "MPG_criteria = MPG_comparison.copy()\n",
        "for col in ['Proyek 1','Proyek 2','Proyek 3']:\n",
        "    MPG_criteria[col] = MPG_criteria[col] / MPG_criteria[col].sum()\n",
        "\n",
        "MPG_P1 = (MPG_criteria['Proyek 1'][0] + MPG_criteria['Proyek 2'][0] + MPG_criteria['Proyek 3'][0])/3\n",
        "MPG_P2 = (MPG_criteria['Proyek 1'][1] + MPG_criteria['Proyek 2'][1] + MPG_criteria['Proyek 3'][1])/3\n",
        "MPG_P3 = (MPG_criteria['Proyek 1'][2] + MPG_criteria['Proyek 2'][2] + MPG_criteria['Proyek 3'][2])/3\n",
        "\n",
        "print(f'Bobot Proyek Berdasarkan Kriteria MPG\\n--------------------------------------\\nProyek 1 : {MPG_P1:.3f}\\nProyek 2 : {MPG_P2:.3f}\\nProyek 3 : {MPG_P3:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "a8d1f876",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "a8d1f876",
        "outputId": "3dbb38df-74ea-453f-edf8-970352c052bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Bobot Proyek')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.bar(['Proyek 1','Proyek 2','Proyek 3'],[MPG_P1,MPG_P2,MPG_P3], color = ['red', 'orange', 'green'])\n",
        "plt.title('Bobot Proyek Berdasarkan Kriteria MPG')\n",
        "plt.ylabel('Bobot Proyek')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac67fa10",
      "metadata": {
        "id": "ac67fa10"
      },
      "source": [
        "Hasil di atas menunjukkan bahwa Proyek 3, pembelian model Ekonomi, memiliki bobot tertinggi 0.639. Ini berarti jika 3 proyek dianalisis berdasarkan kriteria MPG saja, Proyek 3 akan menjadi kandidat terbaik untuk diseleksi."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b3153d56",
      "metadata": {
        "id": "b3153d56"
      },
      "source": [
        "### Kriteria Kenyamanan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "b395b9d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b395b9d7",
        "outputId": "1956eb57-eb3b-45ac-9ecd-0ab3b61fd4de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bobot Proyek Berdasarkan Kriteria Kenyamanan\n",
            "--------------------------------------\n",
            "Proyek 1 : 0.593\n",
            "Proyek 2 : 0.341\n",
            "Proyek 3 : 0.065\n"
          ]
        }
      ],
      "source": [
        "COMFORT_dic = {'KENYAMANAN': ['Proyek 1','Proyek 2','Proyek 3'],\n",
        "                 'Proyek 1': [1,1/2,1/8],\n",
        "                 'Proyek 2': [2,1,1/6],\n",
        "                 'Proyek 3': [8,6,1,]}\n",
        "\n",
        "COMFORT_comparison = pd.DataFrame(COMFORT_dic).set_index('KENYAMANAN')\n",
        "\n",
        "COMFORT_criteria = COMFORT_comparison.copy()\n",
        "for col in ['Proyek 1','Proyek 2','Proyek 3']:\n",
        "    COMFORT_criteria[col] = COMFORT_criteria[col] / COMFORT_criteria[col].sum()\n",
        "\n",
        "COMFORT_P1 = (COMFORT_criteria['Proyek 1'][0] + COMFORT_criteria['Proyek 2'][0] + COMFORT_criteria['Proyek 3'][0])/3\n",
        "COMFORT_P2 = (COMFORT_criteria['Proyek 1'][1] + COMFORT_criteria['Proyek 2'][1] + COMFORT_criteria['Proyek 3'][1])/3\n",
        "COMFORT_P3 = (COMFORT_criteria['Proyek 1'][2] + COMFORT_criteria['Proyek 2'][2] + COMFORT_criteria['Proyek 3'][2])/3\n",
        "\n",
        "print(f'Bobot Proyek Berdasarkan Kriteria Kenyamanan\\n--------------------------------------\\nProyek 1 : {COMFORT_P1:.3f}\\nProyek 2 : {COMFORT_P2:.3f}\\nProyek 3 : {COMFORT_P3:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "4eef4baa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "4eef4baa",
        "outputId": "d72e3dc4-11b8-465f-fe70-f1eb2ffcba6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Bobot Proyek')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.bar(['Proyek 1','Proyek 2','Proyek 3'],[COMFORT_P1,COMFORT_P2,COMFORT_P3], color = ['blue', 'orange', 'red'])\n",
        "plt.title('Bobot Proyek Berdasarkan Kriteria Kenyamanan')\n",
        "plt.ylabel('Bobot Proyek')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f2b4cb6",
      "metadata": {
        "id": "0f2b4cb6"
      },
      "source": [
        "Hasil di atas menunjukkan bahwa Proyek 1, pembelian model Eksekutif, memiliki bobot tertinggi sebesar 0.593. Ini berarti jika 3 proyek dianalisis berdasarkan kriteria Kenyamanan saja, Proyek 1 akan menjadi kandidat terbaik untuk diseleksi."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82d5ea80",
      "metadata": {
        "id": "82d5ea80"
      },
      "source": [
        "### Kriteria Gaya"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "5bc64d57",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bc64d57",
        "outputId": "6346bbf6-60a4-4c57-9102-5dde195fa440"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bobot Proyek Berdasarkan Kriteria Gaya\n",
            "--------------------------------------\n",
            "Proyek 1 : 0.265\n",
            "Proyek 2 : 0.656\n",
            "Proyek 3 : 0.080\n"
          ]
        }
      ],
      "source": [
        "STYLE_dic = {'GAYA': ['Proyek 1','Proyek 2','Proyek 3'],\n",
        "                 'Proyek 1': [1,3,1/4],\n",
        "                 'Proyek 2': [1/3,1,1/7],\n",
        "                 'Proyek 3': [4,7,1,]}\n",
        "\n",
        "STYLE_comparison = pd.DataFrame(STYLE_dic).set_index('GAYA')\n",
        "\n",
        "STYLE_criteria = STYLE_comparison.copy()\n",
        "for col in ['Proyek 1','Proyek 2','Proyek 3']:\n",
        "    STYLE_criteria[col] = STYLE_criteria[col] / STYLE_criteria[col].sum()\n",
        "\n",
        "STYLE_P1 = (STYLE_criteria['Proyek 1'][0] + STYLE_criteria['Proyek 2'][0] + STYLE_criteria['Proyek 3'][0])/3\n",
        "STYLE_P2 = (STYLE_criteria['Proyek 1'][1] + STYLE_criteria['Proyek 2'][1] + STYLE_criteria['Proyek 3'][1])/3\n",
        "STYLE_P3 = (STYLE_criteria['Proyek 1'][2] + STYLE_criteria['Proyek 2'][2] + STYLE_criteria['Proyek 3'][2])/3\n",
        "\n",
        "print(f'Bobot Proyek Berdasarkan Kriteria Gaya\\n--------------------------------------\\nProyek 1 : {STYLE_P1:.3f}\\nProyek 2 : {STYLE_P2:.3f}\\nProyek 3 : {STYLE_P3:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "039cae6e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "039cae6e",
        "outputId": "33204624-4f32-45e9-9350-2b53979a3b01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Bobot Proyek')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.bar(['Proyek 1','Proyek 2','Proyek 3'],[STYLE_P1,STYLE_P2,STYLE_P3], color = ['green', 'red', 'blue'])\n",
        "plt.title('Bobot Proyek Berdasarkan Kriteria Gaya')\n",
        "plt.ylabel('Bobot Proyek')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4d7a743",
      "metadata": {
        "id": "a4d7a743"
      },
      "source": [
        "Hasil di atas menunjukkan bahwa Proyek 2, pembelian model Medium, memiliki bobot tertinggi 0.656. Ini berarti jika 3 proyek dianalisis berdasarkan kriteria Gaya saja, Proyek 2 akan menjadi kandidat terbaik untuk diseleksi."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62892355",
      "metadata": {
        "id": "62892355"
      },
      "source": [
        "### Keempat Kriteria"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "0275600b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0275600b",
        "outputId": "66540914-6504-4c68-f4ee-95e3e3b248d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bobot Proyek Berdasarkan 4 Kriteria\n",
            "--------------------------------------\n",
            "Proyek 1 : 0.265\n",
            "Proyek 2 : 0.421\n",
            "Proyek 3 : 0.314\n"
          ]
        }
      ],
      "source": [
        "Project_1 = Price_weight*PRICE_P1 + Comfort_weight*COMFORT_P1 + MPG_weight*MPG_P1 + Style_weight*STYLE_P1\n",
        "Project_2 = Price_weight*PRICE_P2 + Comfort_weight*COMFORT_P2 + MPG_weight*MPG_P2 + Style_weight*STYLE_P2\n",
        "Project_3 = Price_weight*PRICE_P3 + Comfort_weight*COMFORT_P3 + MPG_weight*MPG_P3 + Style_weight*STYLE_P3\n",
        "\n",
        "print(f'Bobot Proyek Berdasarkan 4 Kriteria\\n--------------------------------------\\nProyek 1 : {Project_1:.3f}\\nProyek 2 : {Project_2:.3f}\\nProyek 3 : {Project_3:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "30873877",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "30873877",
        "outputId": "a59dd4ae-fc8a-435a-b2c3-dd4caa712b6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Bobot Proyek')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.bar(['Proyek 1','Proyek 2','Proyek 3'],[Project_1,Project_2,Project_3], color = ['red', 'orange', 'green'])\n",
        "plt.title('Bobot Proyek Berdasarkan 4 Kriteria')\n",
        "plt.ylabel('Bobot Proyek')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d570d77",
      "metadata": {
        "id": "0d570d77"
      },
      "source": [
        "### Keputusan Akhir"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59892f3c",
      "metadata": {
        "id": "59892f3c"
      },
      "source": [
        "Hasil akhir di atas menunjukkan bahwa proyek terbaik berdasarkan kombinasi keempat kriteria adalah Proyek 2, pembelian model Medium."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}