Prediksi_Jenis_Kelamin.ipynb

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        "import pandas as pd\n",
        "df=pd.read_csv(\"https://raw.githubusercontent.com/irfani/Jenis-Kelamin/master/data/data-pemilih-kpu.csv\", encoding ='utf-8-sig')"
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              "(13137, 2)"
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              "                         nama jenis_kelamin\n",
              "0              ERWIN TJAHJONO     Laki-Laki\n",
              "1  DAVIANDRIE ANDIKA BAHROENY     Laki-Laki\n",
              "2              ELAN KURNIAWAN     Laki-Laki\n",
              "3     AYU DWI CAHYANING MUKTI     Perempuan\n",
              "4                   WAHYOEDIN     Laki-Laki"
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              "                 nama jenis_kelamin\n",
              "13132      HERMANSYAH     Laki-Laki\n",
              "13133         SITA.HJ     Perempuan\n",
              "13134  MASNI TAMBUNAN     Perempuan\n",
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    },
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      "source": [
        "cleansing data"
      ],
      "metadata": {
        "id": "ObNSKFgy7u8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#mengecek apakah ada data yang null\n",
        "df.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdQzm6ej7tkX",
        "outputId": "3721b6c6-f814-4778-b5d3-16ea6cce40fc"
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      "execution_count": 11,
      "outputs": [
        {
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          "data": {
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              "True"
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          "metadata": {},
          "execution_count": 11
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    {
      "cell_type": "code",
      "source": [
        "# mengecek jumlah baris data yang null\n",
        "len(df[pd.isnull(df).any(axis=1)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [
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              "187"
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          "metadata": {},
          "execution_count": 12
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    {
      "cell_type": "code",
      "source": [
        "# menghapus baris yang null\n",
        "df= df.dropna(how='all')\n",
        "len(df[pd.isnull(df).any(axis=1)])"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 13,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
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          "metadata": {},
          "execution_count": 13
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    },
    {
      "cell_type": "code",
      "source": [
        "#cek dimensi setelah di cleansing\n",
        "df.shape\n"
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        "id": "K4jHVCQo8upQ",
        "outputId": "ebb4e292-6e81-4b57-bb0d-fa1c702a0da3"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#mengubah JK dri text menjadi angka (L=1,P=0)\n",
        "jk={\"Laki-Laki\" : 1, \"Perempuan\" : 0}\n",
        "df[\"jenis_kelamin\"]= df[\"jenis_kelamin\"].map(jk)"
      ],
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        "id": "ePw3wrad9AcL"
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              "                         nama  jenis_kelamin\n",
              "0              ERWIN TJAHJONO              1\n",
              "1  DAVIANDRIE ANDIKA BAHROENY              1\n",
              "2              ELAN KURNIAWAN              1\n",
              "3     AYU DWI CAHYANING MUKTI              0\n",
              "4                   WAHYOEDIN              1"
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              "      <td>ELAN KURNIAWAN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WAHYOEDIN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd9762d9-fe44-43b6-b955-ebe667c5e82b')\"\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-cd9762d9-fe44-43b6-b955-ebe667c5e82b 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-cd9762d9-fe44-43b6-b955-ebe667c5e82b');\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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#menghitung jumlah L dan P\n",
        "num_obs= len(df)\n",
        "num_l= len(df.loc[df['jenis_kelamin']==1])\n",
        "num_p= len(df.loc[df['jenis_kelamin']==0])\n",
        "print(\"jumlah Laki-Laki : {0} ({1:2.2f}%)\".format(num_l, (num_l/num_obs)*100))\n",
        "print(\"jumlah Perrempuan : {0} ({1:2.2f}%)\".format(num_p, (num_p/num_obs)*100))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89mMOIs-9kvj",
        "outputId": "76b7840b-293a-48d0-87b1-8a4e826e3d34"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "jumlah Laki-Laki : 6162 (47.58%)\n",
            "jumlah Perrempuan : 6788 (52.42%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualisasi Jumlah Jenis Kelamin\n",
        "import matplotlib.pyplot as plt\n",
        "v1=df['jenis_kelamin'].value_counts()\n",
        "v1.plot(kind='bar')\n",
        "plt.title('Total Jenis Kelamin')\n",
        "plt.ylabel('Total')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "xRTrRo4L_J8_",
        "outputId": "ff9a9195-17b5-444e-b828-10656f3920db"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split Dataset"
      ],
      "metadata": {
        "id": "ad3dnSZzAKzz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "feature_col_name= [\"nama\"]\n",
        "predicted_class_name = [\"jenis_kelamin\"]\n",
        "\n",
        "x=df[feature_col_name].values\n",
        "y=df[predicted_class_name].values\n",
        "split_test_size = 0.30\n",
        "\n",
        "x_train, x_test, y_train, y_test= train_test_split(x,y,test_size=split_test_size)"
      ],
      "metadata": {
        "id": "qzd45iLd_64Y"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "dataset telah dibagi menjadi 2 bagian training 70% dan testing 30%"
      ],
      "metadata": {
        "id": "q2wqkzLsBhoO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"Data Training Laki-Laki : {0}\".format(len(y_train[y_train[:]==1])))\n",
        "print(\"Data Training Perempuan : {0}\".format(len(y_train[y_train[:]==0])))\n",
        "print(\" \")\n",
        "print(\"Data Training Laki-Laki : {0}\".format(len(y_test[y_test[:]==1])))\n",
        "print(\"Data Training Perempuan : {0}\".format(len(y_test[y_test[:]==0])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aqW590qBQbM",
        "outputId": "d1cadbad-622e-4b41-b4f4-c13957b56de1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data Training Laki-Laki : 4318\n",
            "Data Training Perempuan : 4747\n",
            " \n",
            "Data Training Laki-Laki : 1844\n",
            "Data Training Perempuan : 2041\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Extraction**\n",
        "Proses features extraction, berpengaruh terhadap hasil akurasi yang didapatkan nantinya. Disini saya kan menggunakan metode simple yaitu CountVectorizer yang akan membuat matrix frekwensi kemunculan dari suatu karakter di tiap nama yang diberikan, dengan opsi analisa ngram_range 2 - 6 hanya di dalam satu kata saja. Misal Muhammad Irfani Sahnur, menghasilkan n-gram :\n",
        "\n",
        "mu\n",
        "ham\n",
        "mad\n",
        "nur\n",
        "dst\n"
      ],
      "metadata": {
        "id": "t5R604Y1FQ5C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(2,6))\n",
        "vectorizer.fit(x_train.ravel())\n",
        "X_train = vectorizer.transform(x_train.ravel())\n",
        "X_test = vectorizer.transform(x_test.ravel())"
      ],
      "metadata": {
        "id": "SPkyweEMENgp"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Percobaan pertama menggunakan metode Logistic Regression**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "-sOQ8LMkFi-K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "Lr= LogisticRegression ()\n",
        "Lr.fit(X_train,y_train.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "of82kLrhFPpG",
        "outputId": "0e63c4c2-932b-46c8-9641-9b0bde848661"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Akurasi Training : \",Lr.score(X_train, y_train))\n",
        "print(\"Akurasi Testing : \", Lr.score(X_test,y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xL_w5KlwIEZd",
        "outputId": "d2acd6c5-47dd-4a9a-e60d-fb894abf7541"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Training :  0.9961389961389961\n",
            "Akurasi Testing :  0.9405405405405406\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Akurasi prediksi menggunakan Logististic Regression adalah **94,05%**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "0tvoB9cwIeCb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**detail akurasi metrik**"
      ],
      "metadata": {
        "id": "w6OPgAWPI_O3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "Lr_predict=Lr.predict(X_test)\n",
        "\n",
        "print(\"Accuracy : {0:.4f}\".format(metrics.accuracy_score(y_test,Lr_predict)))\n",
        "print(metrics.confusion_matrix(y_test,Lr_predict,labels=[1,0]))\n",
        "print(\" \")\n",
        "print(\"Classification Report\")\n",
        "print(metrics.classification_report(y_test,Lr_predict,labels=[1,0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfcEOldLI3ZN",
        "outputId": "804c2bcc-34fc-43eb-abd9-38450ecb7611"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy : 0.9405\n",
            "[[1746   98]\n",
            " [ 133 1908]]\n",
            " \n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.93      0.95      0.94      1844\n",
            "           0       0.95      0.93      0.94      2041\n",
            "\n",
            "    accuracy                           0.94      3885\n",
            "   macro avg       0.94      0.94      0.94      3885\n",
            "weighted avg       0.94      0.94      0.94      3885\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testing Prediksi Jenis Kelamin\n",
        "Jk_label={1:\"Laki-Laki\", 0:\"Perempuan\"}\n",
        "test_predict = vectorizer.transform([\"pandi barep arianza\"])\n",
        "res = Lr.predict(test_predict)\n",
        "\n",
        "print(Jk_label[int(res)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mt6K0zE6Kb_d",
        "outputId": "f78f23f9-c88c-489f-c4c2-d7e904937956"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Laki-Laki\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Percobaan kedua menggunakan metode Naive Bayes**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "9CvO7-khLXjV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "import numpy as np\n",
        "clf_nb = Pipeline([('vect', CountVectorizer(analyzer = 'char_wb', ngram_range=(2,6))),\n",
        "                     ('Lr', MultinomialNB()),\n",
        "])\n",
        "clf_nb=clf_nb.fit(x_train.ravel(),y_train.ravel())\n",
        "predicted= clf_nb.predict(x_test.ravel())\n",
        "np.mean(predicted==y_test.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTn266obLj2X",
        "outputId": "5151049d-4a4b-4294-c620-fe591064f3e0"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.931016731016731"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "akurasi yang didapatkan menggunakan naive bayes yaitu **93,10%** \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_ncdKbGxP1pV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result=clf_nb.predict([\"shelya renanda\"])\n",
        "print(Jk_label[result[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9hKnYMYQCIG",
        "outputId": "cefa5f28-1df2-4156-baab-48f50eb0f5e6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perempuan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Percobaan Ketiga menggunakan Random Forest**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "b2eLlzawQeZ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "clf_rf = Pipeline([('vect', CountVectorizer(analyzer = 'char_wb', ngram_range=(2,6))),\n",
        "                     ('Lr', RandomForestClassifier()),\n",
        "])\n",
        "clf_rf=clf_rf.fit(x_train.ravel(),y_train.ravel())\n",
        "predicted=clf_rf.predict(x_test.ravel())\n",
        "np.mean(predicted==y_test.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4p3IXJUQaIW",
        "outputId": "c261eb34-41e0-4199-f4d5-91e60a208f60"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9328185328185328"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "setelah menggunakan Random Forest akurasi yang didapatkan sebesar **93,28%**."
      ],
      "metadata": {
        "id": "AtxcinVZRvmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = clf_rf.predict(['bisma septian'])\n",
        "print(Jk_label[result[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2VcBNjCR8o2",
        "outputId": "c5e1c15c-21ee-4b9b-a9e6-6b78f673fc31"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Laki-Laki\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = clf_rf.predict(['abi perdana khusuma'])\n",
        "print(Jk_label[result[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mwVNb1aSJUb",
        "outputId": "2d60ac8d-c375-4aaf-831b-7ae03ed32dbf"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Laki-Laki\n"
          ]
        }
      ]
    }
  ]
}