KOMPUTASI BIG DATA_ACT_PERT_1.ipynb

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      "source": [
        "#KUMAL_KBD_ACT_PERT 1"
      ],
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        "id": "gtb_UwiQaLb4"
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      "source": [
        "##SOAL\n",
        "\n",
        "# Instruksi Tugas Praktikum FIKTI dan FTI\n",
        "\n",
        "1. Silahkan modifikasi kode operasi yang ada menggunakan library perhitungan berbasis GPU (Library Cupy)\n",
        "2. Jelaskan apakah terdapat korelasi antara variabel atau fitur compactness_mean dengan concavity_mean\n",
        "3. Buatlah diagram korelasi dari fitur compactness_mean dengan concavity_mean\n",
        "4. Hitung nilai covariance dari fitur compactness_mean dengan concavity_mean. Jelaskan arti dari nilai tersebut.\n",
        "5. Hitung nilai pearson correlation dari fitur compactness_mean dengan concavity_mean. Jelaskan arti dari nilai tersebut.\n",
        "6. Lakukan uji hipotesis untuk kolom fitur compactness_mean dengan concavity_mean yang berbeda serta berikan penjelasan terhadap hasil dari uji hipotesis yang dilakukan. \n"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1) Silahkan modifikasi kode operasi yang ada menggunakan library perhitungan berbasis GPU (Library Cupy)"
      ],
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        "id": "hV9924m2-4cF"
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        "id": "b4Rnu5az-F44"
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      "outputs": [],
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import cupy as cp\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas.plotting import autocorrelation_plot\n",
        "from scipy import stats\n",
        "plt.style.use(\"ggplot\")\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read data as pandas data frame\n",
        "url_data = \"https://raw.githubusercontent.com/supasonicx/ATA-praktikum-01/main/data.csv\"\n",
        "data = pd.read_csv(url_data)\n",
        "data = data.drop(['Unnamed: 32','id'],axis = 1)"
      ],
      "metadata": {
        "id": "E0_HClr--JbR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Melihat 5 baris awal dari dataset yang digunakan\n",
        "data.head()"
      ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
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            "text/plain": [
              "  diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "0         M        17.99  ...          0.4601                  0.11890\n",
              "1         M        20.57  ...          0.2750                  0.08902\n",
              "2         M        19.69  ...          0.3613                  0.08758\n",
              "3         M        11.42  ...          0.6638                  0.17300\n",
              "4         M        20.29  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 31 columns]"
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      "source": [
        "# 2) Jelaskan apakah terdapat korelasi antara variabel atau fitur compactness_mean dengan concavity_mean"
      ],
      "metadata": {
        "id": "TwPT5EZ7-9oA"
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              "      <th></th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
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              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.883121</td>\n",
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              "      <th>concavity_mean</th>\n",
              "      <td>0.883121</td>\n",
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              "      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-40ccd2fa-8b1d-474a-b7ff-38a720c06768 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-40ccd2fa-8b1d-474a-b7ff-38a720c06768');\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",
              "  "
            ],
            "text/plain": [
              "                  compactness_mean  concavity_mean\n",
              "compactness_mean          1.000000        0.883121\n",
              "concavity_mean            0.883121        1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PENJELASAN\n",
        "\n",
        "diagram korelasi antara variable compactness_mean dengan concavity_mean menunjukan bahwa kedua diagram korelasi nya positif satu sama lain\n",
        "\n",
        "memiliki korelasi antara compactness_mean dengan concavity_mean dengan nilai 0.8 atau 80%"
      ],
      "metadata": {
        "id": "4wgdViSmAY4w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Buatlah diagram korelasi dari fitur compactness_mean dengan concavity_mean"
      ],
      "metadata": {
        "id": "4-ZEYTdZAS2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f,ax=plt.subplots(figsize = (18,18))\n",
        "sns.heatmap(datacorr1.corr(),annot= True,linewidths=0.5,fmt = \".1f\",ax=ax)\n",
        "plt.xticks(rotation=90)\n",
        "plt.yticks(rotation=0)\n",
        "plt.title('Correlation Map')\n",
        "plt.savefig('graph.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "blioWoCc_8sc",
        "outputId": "714bd759-495b-4609-f23c-f939ec9a407c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1296 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (25,20))\n",
        "sns.jointplot(data.compactness_mean,data.concavity_mean,kind=\"reg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hEeiauOmcJtr",
        "outputId": "96be6215-5bed-4435-af50-34805ac05564",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1440 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#PENJELASAN\n",
        "\n",
        "terdapat grafik yang menggambarkan sebuah diagram korelasi yang berupa grafik dan blok lalu nanti angak tersebut akan di hitung di nomor berikut nya ini merupakan tampilan visual dari grafik diagram korelasi"
      ],
      "metadata": {
        "id": "PBBbQRdycvyK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) Hitung nilai covariance dari fitur compactness_mean dengan concavity_mean. Jelaskan arti dari nilai tersebut."
      ],
      "metadata": {
        "id": "OZuoVyUzAmTT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cp.cov(data.compactness_mean,data.concavity_mean)\n",
        "print(\"Covariance diantara compactness mean dan concavity mean: \",data.compactness_mean.cov(data.concavity_mean))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WJ9ZQ1CAFNl",
        "outputId": "d842fa7e-36cd-4587-a272-ffa4831f63d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance diantara compactness mean dan concavity mean:  0.003718134921780181\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PENJELASAN\n",
        "\n",
        "nilai covariance tidak 0 berarti dua kolom ini bukan ortogonal dan tidak negatif berarti menunjuk ke arah yang sama"
      ],
      "metadata": {
        "id": "5uT_SfqXBSPq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5) Hitung nilai pearson correlation dari fitur compactness_mean dengan concavity_mean. Jelaskan arti dari nilai tersebut."
      ],
      "metadata": {
        "id": "0GK-G5feBeRu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p1 = data.loc[:,[\"compactness_mean\",\"concavity_mean\"]].corr(method= \"pearson\")\n",
        "p2 = data.compactness_mean.cov(data.concavity_mean)/(data.compactness_mean.std()*data.concavity_mean.std())\n",
        "print('Pearson correlation: ')\n",
        "print(p1)\n",
        "print('Pearson correlation: ',p2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uk4raCdbA6l3",
        "outputId": "59880407-be69-44bb-add3-f613f6751dbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson correlation: \n",
            "                  compactness_mean  concavity_mean\n",
            "compactness_mean          1.000000        0.883121\n",
            "concavity_mean            0.883121        1.000000\n",
            "Pearson correlation:  0.8831206701772506\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PENJELASAN\n",
        "\n",
        "Pearson korelasi antara compactness dan concavity berkorelasi positif dikarenakan nilai korelasinya 0.88 angka yang dapat menunjukan korelasi yang positif"
      ],
      "metadata": {
        "id": "A0LuVvNGB-T4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6) Lakukan uji hipotesis untuk kolom fitur compactness_mean dengan concavity_mean yang berbeda serta berikan penjelasan terhadap hasil dari uji hipotesis yang dilakukan."
      ],
      "metadata": {
        "id": "t2xBpZtUCPJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "statistic, p_value = stats.ttest_rel(data.compactness_mean,data.concavity_mean)\n",
        "print('p-value adalah: ',p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXKV-KMAB3ye",
        "outputId": "73882a59-0229-4f20-f057-4d999d239429"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p-value adalah:  4.2923131591850576e-18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#PENJELASAN\n",
        "\n",
        "p-value rendah, makadari itu pengaruh tersebut dikatakan signifikan secara statistik yang artinya tidak mungkin terjadi secara kebetulan. Nilainya mendekati 0 berarti yang berarti kedua data ini sangat berpengaruh dan dapat mengabaikain hipotesis menjadi 0"
      ],
      "metadata": {
        "id": "4qrUy0s3DEH1"
      }
    }
  ]
}