MIF_E31222492/Pengumpulan Data/Model-SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[500  71  20]\n",
      " [ 15 558  18]\n",
      " [ 12  45 534]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Negatif       0.95      0.85      0.89       591\n",
      "      Netral       0.83      0.94      0.88       591\n",
      "     Positif       0.93      0.90      0.92       591\n",
      "\n",
      "    accuracy                           0.90      1773\n",
      "   macro avg       0.90      0.90      0.90      1773\n",
      "weighted avg       0.90      0.90      0.90      1773\n",
      "\n",
      "\n",
      "Cross-Validation Scores:\n",
      "[0.81556684 0.82177101 0.87986464 0.94190637 0.91878173]\n",
      "Mean Cross-Validation Score: 0.8756\n",
      "Standard Deviation of Cross-Validation Scores: 0.0506\n"
     ]
    }
   ],
   "source": [
    "# Import library yang diperlukan\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "\n",
    "# Membaca dataset yang sudah di-balance\n",
    "df_balanced = pd.read_csv('data-analisis/datasets-balanced.csv')\n",
    "\n",
    "# Memisahkan fitur dan label\n",
    "X = df_balanced.drop(columns=['label'])  # Menghapus kolom label\n",
    "y = df_balanced['label']  # Mengambil kolom label\n",
    "\n",
    "# Membagi dataset menjadi data latih (80%) dan data uji 20%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Membuat model SVM\n",
    "svm_model = SVC(kernel='linear', random_state=42)  # Anda bisa mengganti kernel sesuai kebutuhan\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# Melakukan prediksi pada data uji\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "# Menampilkan hasil evaluasi\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Evaluasi model menggunakan cross-validation\n",
    "cv_scores = cross_val_score(svm_model, X, y, cv=5)  # Menggunakan 5-fold cross-validation\n",
    "\n",
    "# Menampilkan hasil cross-validation\n",
    "print(\"\\nCross-Validation Scores:\")\n",
    "print(cv_scores)\n",
    "print(f\"Mean Cross-Validation Score: {cv_scores.mean():.4f}\")\n",
    "print(f\"Standard Deviation of Cross-Validation Scores: {cv_scores.std():.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Generate confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Subplot for Confusion Matrix\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "\n",
    "# Subplot for Cross-Validation Scores\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=cv_scores, orient='w')\n",
    "plt.title('Cross-Validation Scores')\n",
    "plt.xlabel('Score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "import joblib # type: ignore\n",
    "\n",
    "with open('model-svm.pkl', 'wb') as file:\n",
    "    joblib.dump(svm_model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Menampilkan hasil evaluasi\n",
    "class_report = classification_report(y_test, y_pred, output_dict=True)  # Get report as dict\n",
    "\n",
    "# # Save the model\n",
    "# joblib.dump(svm_model, 'HASIL-RISET/svm_model-new.')  # Save the model to a file\n",
    "\n",
    "# Save evaluation results\n",
    "results = {\n",
    "    'Confusion Matrix': [conf_matrix.flatten()],  # Flatten for easier saving\n",
    "    'Classification Report': [class_report],\n",
    "    'Cross-Validation Scores': [cv_scores.tolist()],\n",
    "    'Mean CV Score': [cv_scores.mean()],\n",
    "    'Std Dev CV Score': [cv_scores.std()]\n",
    "}\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df.to_csv('HASIL-RISET/evaluation_results_SVM-new.csv', index=False)  # Save results to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ovumpaXl6vPk7M/xldx22234+vry6quvXjQqarfbL/qc5tXChQuzzNHcu3cvU6ZMoXXr1pmjtb6+vhdlGTFixEVbSd18880sWrSIJUuWZN525MiRi0a3zx33/GOmpKTw8ccf5/n5XOpn61KPBzBs2LCLPv5yX+tLHSMuLo6xY8dmK9e5n5+RI0de9L5Ro0ZluY+It9GIqohBf/zxB5s2bSItLY1Dhw4xc+ZM/v77b0qXLs0vv/xyxVHF1157jblz59K+fXtKly7N4cOH+fjjjylRogTXX3894CiNUVFRfPrpp4SHhxMaGkrDhg0z59/lVMGCBbn++uu5//77OXToEMOGDaN8+fJZttB66KGH+Omnn2jbti1dunRh+/btfPPNN5QrVy7LsVq3bk1MTAxNmjShaNGibNy4kZEjR9K+ffsrzs29lFtuuYUyZcrQsWNHypUrx+nTp5k+fTq//vor9evXp2PHjgA0b96c3r178/bbb7Nq1Spat26Nv78/W7duZeLEiXz00UfccccdOXrsWrVq4evry5AhQ4iLiyMwMDBzP01nK1euHG+88QbPP/88u3bt4tZbbyU8PJydO3fyv//9j169evH000877fGqV69OmzZtsmxPBWSZmtGhQwfGjx9PZGQkVatWZeHChUyfPp1ChQplOdazzz7L+PHjadu2LU888UTm9lSlS5fOcqWoxo0bU6BAAXr06EG/fv2w2WyMHz8+x9MVsvuzFRERQbNmzXj33XdJTU2lePHi/PXXX5ccwT23sOzFF1+ka9eu+Pv707FjR1q3bk1AQAAdO3akd+/eJCQkMHr0aIoUKZI5mn8ltWrV4qGHHuKjjz5i69at3HTTTYBj26zff/+dhx56iGuvvTZHz1/EY+T7PgMikrmFzrmXgIAAe0xMjP2mm26yf/TRR1m2gDrnwu2pZsyYYb/lllvsxYoVswcEBNiLFStmv/vuu+1btmzJ8nFTpkyxV61a1e7n55dlq6rmzZtfdnuoy22r9N1339mff/55e5EiRezBwcH29u3b23fv3n3Rx3/wwQf24sWL2wMDA+1NmjSxL1u27KJjfvbZZ/ZmzZrZCxUqZA8MDLSXK1fO/swzz9jj4uIu+jxdbXuq7777zt61a1d7uXLl7MHBwfagoCB71apV7S+++OIlP5eff/65vW7duvbg4GB7eHi4vUaNGvZnn33Wvn///sz7lC5d2t6+ffurfm7sdrt99OjR9rJly9p9fX2zbBt0uc/jxIkTs3z85bZUOvc1v3ALskmTJtmvv/56e2hoqD00NNReuXJle58+feybN2/OkvNSX9/sbmME2Pv06WP/5ptv7BUqVLAHBgbaa9eufdE2XCdOnLDff//99sKFC9vDwsLsbdq0sW/atOmS25StWbPG3rx5c3tQUJC9ePHi9tdffz1za7Hzv8bz58+3X3fddfbg4GB7sWLF7M8++6x92rRpl90G7Hy5+dnat2+fvXPnzvaoqCh7ZGSk/c4777Tv37/fDthfeeWVLPd9/fXX7cWLF7f7+Phkyf3LL7/Ya9asaQ8KCrLHxsbahwwZYv/yyy+z9f1rt9vt6enp9o8++sh+7bXX2oOCguxBQUH2a6+91j58+PCLtlnT9lTiTWx2uxNn1YuIiEew2Wz06dPnkqejxawZM2bQqlUr/vnnn8yzJyKeSnNURURE3Mi56QTnro4l4slUVEVExG2cu8791RavxcbGZlkQdfDgQW666abMfU7d0enTp/n88895++23KVGiRL7s/CBimoqqiIg4Xc+ePbHZbNhsNgICAihfvjyvvfZa5lW2cqtx48YcOHAg8yIJ48aNu2TxXLp0Kb169cp8e+jQoRw4cIBVq1axZcuWPGUw5ciRIzz++OMEBwczadIkfHz0X7h4Pq36FxGRizhj+ULbtm0ZO3YsycnJ/P777/Tp0wd/f3+ef/75XB8zICCAmJiYq97vwos3bN++nbp1617y6ljuIjY2NnPrORFvoT/HRETEJQIDA4mJiaF06dI8+uijtGrVil9++YUTJ07QvXt3ChQoQEhICO3atcuywf7u3bvp2LEjBQoUIDQ0lGrVqmXuiXv+qf/Zs2dz//33ExcXlzl6O3jwYCDrqf/Y2FgmTZrE119/jc1mo2fPnvn8mRCR3NKIqoiI5Ivg4GCOHTtGz5492bp1K7/88gsREREMHDiQm2++mQ0bNuDv70+fPn1ISUlh7ty5hIaGsmHDhosuDwuOaQDDhg1j0KBBmVe4utT9li5dSvfu3YmIiOCjjz5yyaWDRcQ1VFRFRMSl7HY7M2bMYNq0abRr147Jkyczf/58GjduDMCECRMoWbIkkydP5s4772TPnj3cfvvt1KhRA+CiS++eExAQQGRkJDab7YrTAaKjowkMDCQ4ODhb0wZExDp06l9ERFxi6tSphIWFERQURLt27bjrrrvo2bMnfn5+NGzYMPN+hQoVolKlSmzcuBGAfv368cYbb9CkSRNeeeWVLFeuEhHvoqIqIiIu0aJFC1atWsXWrVs5c+YMX331FTab7aof99BDD7Fjxw7uu+8+1q5dS7169RgxYkQ+JBYRq1FRFRERlwgNDaV8+fKUKlUKPz/HTLMqVaqQlpbG4sWLM+937NgxNm/eTNWqVTNvK1myJI888gg///wzTz31FKNHj77kYwQEBJCenu7aJyIixqioiohIvqlQoQK33HILDz/8MPPmzWP16tXce++9FC9enFtuuQWA/v37M23aNHbu3MmKFSuYNWsWVapUueTxYmNjSUhIYMaMGRw9epTExMT8fDoi4mIqqiIikq/Gjh1L3bp16dChA40aNcJut/P777/j7+8PQHp6On369KFKlSq0bduWihUr8vHHH1/yWI0bN+aRRx7hrrvuIjo6mnfffTc/n4qIuJjN7oxdnUVEREREnEwjqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJfqYDiIh4CrvdzunU0ySkJBCfHE98Snz2/332dVJaEr42X/x8/PD1Ofv67NuXuu2it8++DgsIo1BIIQqHFKZwSGEKBf/374LBBbHZbKY/XSIiV6WiKiKSDfHJ8ew9tZd9p/axN24ve0/t/e/1qb3sj99PQkoCGfYM01GvytfmS4HgApcssRe+lIgoQfHw4iq2ImKEzW63202HEBExKSkt6eLyeV4J3Ru3l7jkONMxjQn0DaR0VGnKFihLmagyWV6XLVCWyKBI0xFFxEOpqIqI10hKS2LDkQ2sObSGtYfWsubwGtYdXsfBhIOmo7m16JBoKheuTKVClahUuFLm67IFyuLnoxN3IpJ7Kqoi4nHsdjs7T+5k7aG1rD281lFMD69l67GtpNvTTcfzGv4+/pQtUJYq0VWoE1OH+sXrU79YfQqFFDIdTUTchIqqiLi1E2dO/FdGz46Srj+8nviUeNPR5DLKRJXJLK31i9WnbrG6hAWEmY4lIhakoioibsNut7P28Frm7JrD3D1zWbRvEftO7TMdS/LIx+ZD5cKVaVC8QWZ5vTbmWgJ8A0xHExHDVFRFxLLSM9JZcWAFc3fPZc7uOczbM48TSSdMx5J8EOAbQM2iNTOLa/3i9akaXRUfm7b/FvEmKqoiYhkp6Sks/Xcpc3bPYe7uuSzYu0Cn8CVTgaAC3Fj2RtqUa0Obcm0oGVnSdCQRcTEVVREx5kzqGRbuW5g5Yrp432LOpJ0xHUvcRKVClWhdrjVtyrXhhtgbCA0INR1JRJxMRVVE8tXKAyuZsnkKf+/4m2X7l5GSnmI6kniAAN8AGpdsTOuyrWlTvg21Y2rrIgUiHkBFVURcKi0jjbm75zJ502R+2fwLu+N2m44kXiA6JJpWZVvRulxrWpdrTbHwYqYjiUguqKiKiNMlpiby57Y/mbxpMr9t/Y3jZ46bjiRernqR6rQu25r2FdvTvHRzfH18TUcSkWxQURURpzhy+gi/bP6FKZunMH3HdM01FcsqGlqUO6reQdfqXWlSsommCIhYmIqqiOTa9uPbmbxpMpM3T2bB3gVk2DNMRxLJkZIRJelSrQtdq3elXrF6puOIyAVUVEUkR1YfXM3EDROZvGky64+sNx1HxGnKFyzPXdXu4u7qd1OtSDXTcUQEFVURyYZDCYeYsHYCX63+ijWH1piOI+Jy1aKr0bV6V+6ufjflCpYzHUfEa6moisglJacl88vmX/hq9VdM2z6NtIw005FEjKh7TV26Vu/KXdXu0kUGRPKZiqqIZLF432LGrRrHD+t/0OVKRc5jw0bjko3pcW0PutXsRoh/iOlIIh5PRVVEOH7mOONXj2f0itGadyqSDZGBkfS4tgeP1X+MSoUrmY4j4rFUVEW82Jxdcxi9YjSTNk4iKS3JdBwRt2PDRssyLelTvw+dKnXS/qwiTqaiKuJljpw+wrhV4/hi5RdsObbFdBwRj1EiogS96/bm4ToPUzSsqOk4Ih5BRVXES6w9tJb3FrzHD+t/ICU9xXQcEY/l7+PP7VVv57F6j9G0dFPTcUTcmoqqiIebsWMG7y14j2nbp5mOIuJ1ahatyWP1HuPemvcSGhBqOo6I21FRFfFA6RnpTNwwkfcWvMeKAytMxxHxehGBEXSv2Z3H6j9GlegqpuOIuA0VVREPcjrlNGNWjmHooqHsOrnLdBwRuYQby9zIC01foGWZlqajiFieiqqIBzh8+jAjFo/g42Ufc/zMcdNxRCQbri91PS83e5nW5VqbjiJiWSqqIm5s67GtvL/gfb5e87W2lxJxU9eVuI6Xm73MzRVuNh1FxHJUVEXc0KJ9i3h3/rtM2TyFDHuG6Tgi4gT1itXj5WYv06lSJ9NRRCxDRVXEjfy57U/e/OdN5u2ZZzqKiLhI7ZjavNTsJTpX7ozNZjMdR8QoFVURN7Dk3yUMnD6Q2btmm44iIvmkRpEavNTsJe6oegc+Nh/TcUSMUFEVsbDNRzfz4swXmbRxkukoImJI1eiqvNj0RbpW76rCKl5HRVXEgvbH72fw7MGMXTWWtIw003FExAIqFarEC01foFuNbvj6+JqOI5IvVFRFLCQuKY4h84fw0eKPSExNNB1HRCyoYqGKvHfTe1p0JV5BRVXEApLTkhmxZARvz3tb+6CKSLa0KtuKoW2GUr1IddNRRFxGRVXEoAx7Bl+t+opXZr/C3lN7TccRETfja/OlV91evNbiNQqHFDYdR8TpVFRFDPl186+8MPMF1h1eZzqKiLi5qKAoXmn+Cn3q98Hf1990HBGnUVEVyWcL9i5g4PSB2gtVRJyuUqFKfND6A9pXbG86iohTqKiK5JNDCYd48q8n+Xbtt6ajiIiHa1OuDUPbDKVKdBXTUUTyREVVxMXsdjufL/+c52Y8x8mkk6bjiIiX8PPx49F6jzL4hsEUDC5oOo5IrqioirjQ2kNr6T21Nwv3LTQdRUS8VMHgggxuPphH6z+Kn4+f6TgiOaKiKuICiamJDJ49mKGLhmrDfhGxhKrRVfmw9Ye0Kd/GdBSRbFNRFXGy37b8Rp/f+7A7brfpKCIiF7mj6h2MunkURUKLmI4iclUqqiJO8u+pf3nizyeYtHGS6SgiIldUOKQwI9qNoGv1rqajiFyRiqpIHmXYMxi5ZCQvzXyJ+JR403FERLLt1sq38kn7T4gJizEdReSSVFRF8mD5/uX0ntqb5QeWm44iIpIrBYMLMqzNMO679j7TUUQuoqIqkgvxyfG8NPMlRi0dRbo93XQcEZE8a1+hPZ91+IziEcVNRxHJpKIqkkN/bP2Dh399mH/j/zUdRUTEqSIDI/mwzYc8UPsB01FEABVVkWxLSkvimb+eYeTSkaajiIi4VJtybRjdcTQlI0uajiJeTkVVJBvWHlrL3ZPuZv2R9aajiIjki4jACN676T161e1lOop4MRVVkSuw2+0MXzycgdMHkpyebDqOiEi+u7HMjXzR6Qtio2JNRxEvpKIqchkHEw5y/5T7+XPbn6ajiIgYFRYQxjs3vsNj9R/DZrOZjiNeREVV5BKmbpnKA1Me4EjiEdNRREQso135dnzd+WsKhxQ2HUW8hIqqyHnOpJ7hqb+e4pNln5iOIiJiScXDizPhtgk0j21uOop4ARVVkbNWH1zNPT/fw4YjG0xHERGxNF+bLy83e5mXm7+Mj83HdBzxYCqq4vXsdjtDFw3l+RnPk5KeYjqOiIjbaBHbggm3TeCa8GtMRxEPpaIqXu1A/AF6TO7B3zv+Nh1FRMQtRYdEM77zeNqUb2M6inggFVXxWr9u/pUHfnmAo4lHTUcREXFrNmw8d/1zvN7idXx9fE3HEQ+ioipex26388rsV3hj7hvY0be/iIiz3FjmRr67/TuiQ6NNRxEPoaIqXiU+OZ77/ncfUzZPMR1FRMQjFQ8vzsQ7J9KoZCPTUcQDqKiK19h+fDu3fH+LLoMqIuJi/j7+vN/6ffo17Gc6irg5FVXxCn9v/5u7frqLE0knTEcREfEad1W7iy86fUFYQJjpKOKmVFTF4w1dOJRn/n6GdHu66SgiIl6nSuEq/HzXz1QuXNl0FHFDKqrisZLTkuk9tTdfrf7KdBQREa8WFRTFz11+pkWZFqajiJtRURWPtD9+P51/6MySf5eYjiIiIjjmrY7uOJoetXqYjiJuRNc9E4+zaN8i6n1eTyVVRMRCUjNS6TmlJ4NmDTIdRdyIRlTFo4xbNY5Hpj5Ccnqy6SgiInIZ3Wp0Y0ynMQT6BZqOIhanoioeIS0jjaemPcXwJcNNRxERkWxoWqopk7tOpmBwQdNRxMJUVMXtHT9znC4TuzBj5wzTUUREJAcqFqrI7/f8TrmC5UxHEYtSURW3tiduDzeNv4ktx7aYjiIiIrlQOKQwk++aTJNSTUxHEQvSYipxWxuPbKTJl01UUkVE3NjRxKPc+PWN/LDuB9NRxIJUVMUtLf13KU3HNmXfqX2mo4iISB4lpydz96S7eeuft0xHEYvRqX9xOzN2zODWH24lISXBdBQREXGyB2o9wKcdPsXf1990FLEAFVVxKz9v/Jl7Jt2j7adERDzYjWVuZFKXSUQGRZqOIoapqIrb+GLFFzwy9RHS7emmo4iIiItVi67G3/f9zTXh15iOIgZpjqq4hSHzhvDwrw+rpIqIeIn1R9bTfFxzrUXwchpRFct79u9neW/Be6ZjiIiIAWULlGVm95mUjiptOooYoKIqlpWekU6vX3vx5aovTUcRERGDSkeWZlaPWZQpUMZ0FMlnKqpiSclpjq1K/rfpf6ajiIiIBZSIKMGsHrMoX7C86SiSj1RUxXLik+O59YdbmblzpukoIiJiIcXCizGz+0wqFa5kOorkExVVsZSjiUdpN6Edy/YvMx1FREQsqGhoUWb2mEnV6Kqmo0g+UFEVyziWeIwbvrqBdYfXmY4iIiIWFh0SzfTu06lZtKbpKOJi2p5KLCEuKY7W37RWSRURkas6kniEll+1ZOWBlaajiIupqIpxp1NO0/7b9qw4sMJ0FBERcRPHzhzjxq9v1FQxD6eiKkYlpyVzy/e3MH/vfNNRRETEzZxIOkGrr1uxaN8i01HERVRUxZjU9FTumHgHM3bOMB1FRETcVFxyHK3Ht2bennmmo4gLqKiKEekZ6dz7v3uZumWq6SgiIuLm4lPiaftNW2bvmm06ijiZiqrkO7vdzkO/PsSP6380HUVERDzE6dTTdPyuI8v3LzcdRZxIRVXy3eN/PM64VeNMxxAREQ+TkJLAzd/ezPbj201HESdRUZV8NfDvgYxaOsp0DBER8VCHTx+mzTdtOJRwyHQUcQIVVck3r895nXcXvGs6hoiIeLjtJ7Zz87c3k5CSYDqK5JGKquSLoQuHMmj2INMxRETES6w4sILbfriN1PRU01EkD1RUxeU+W/YZT/71pOkYIiLiZf7e8Tc9p/REV4t3Xyqq4lLfrPmGx35/zHQMERHxUt+u/Zan/3radAzJJZtdf2aIi/y1/S/af9uetIw001FERMTLvXfTezzdWIXV3aioikusO7yOJl824VTyKdNRREREsGHj685fc2/Ne01HkRxQURWnO5hwkIZfNGRP3B7TUURERDL5+/gz9Z6ptC7X2nQUySbNURWnSkxNpNN3nVRSRUTEclIzUrn9x9tZtn+Z6SiSTSqq4jQZ9gzu/flelu5fajqKiIjIJSWkJND+2/ZsO77NdBTJBhVVcZpn/36W/236n+kYIiIiV6SrV7kPFVVxirHLvuCDhR+YjiEiIpItO07soNP3nUhOSzYdRa5ARVXybtYsWt3/OtdGVDSdREREJNuW/LuEvr/3NR1DrkCr/iVvduyABg3g2DESCoTSbVA1folbYjqViIhItn3e4XMervuw6RhyCSqqknvx8XDddbBhQ+ZNGT42nnutGe+lzTEYTEREJPsCfAOY23MuDUs0NB1FLqBT/5I7GRlwzz1ZSiqAT4add1+aw5dHryfAN8BQOBERkexLSU/h9h9v5/Dpw6ajyAVUVCV3XnwRpk697LvvHzmPv5dWplBQwXwMJSIikjv/xv/LnRPv1GW/LUZFVXJuwgR4552r3q3ZL2tY8m0oVSLK5kMoERGRvJm7ey5P//W06RhyHs1RlZxZvdoxLzUpKdsfEhcdwV0vVGBa3HIXBhMREXGObzp/Q7ea3UzHEFRUJScSEqBuXdiyJccfmu7nQ//Xr2dk8lwXBBMREXGeYL9gFjy4gFoxtUxH8XoqqpJ9997rOO2fB5882ZR+UQs1B0gkt5aefTl59u0iQHOgApAIzAa2A3FACFAZaAkEXeGYs4B1wCnAF7gGuBEocfb9acAvwCYgDGgPlDvv4+effbyb8/C8RCymTFQZlvVaRsFgrbUwSUVVsmfMGHjoIaccavrttbmz3g5OJsc55XgiXmUzYAMKAXZgNY6i+MjZt2cDtYBoHGV2KlAUuOsKx1wDhAIFcJTShcAGoN/Z2xfjKMddgK1nH++ZszlOAOOBXly5DIu4oTbl2vB7t9/xsWlJjyn6zMvVrV8P/fo57XCtJq1k0aSClA8v7bRjiniNSkBFHEW1MI6RzwBgH/8V0kpAQaDs2fdvAdKvcMyaOEZIC+IYoW0DJAPnLoN+5OwxiwANcIzcJp5931TgJlRSxSNN2z6Nl2a+ZDqGV1NRlStLTIQuXRyvnajSsp0sfj+OGyJrOfW4Il4lA1gLpPLfafoLJQGBOE7pZ0casPzsxxQ9e1sMsOfs42zDcfo/BMdIrB9QJRfZRdzEO/Pe4X8b/2c6htfSqX+5sgcegLFjXXb41EA/Hnu9EV8k/uOyxxDxOIeAL3CUygDgdhyjrBc6DXyOY8T0xqscczPwE44yGg50BYqffV868CeO0/4hOEZco4HRQE9gGY45rgWBW4CIXD0rEcsKDwhn8UOLqRKtv8rym4qqXN4338B99+XLQ334XHOeCf6HDHtGvjyeiFtLw7F4KRnHXNIVOApjkfPuk4Rj7mgwcDdXH1FNAeJxnNJfAewEHsIxenopk3GMtEYBM4CHccxdPcyV58OKuKlq0dVY3ms5gX6BpqN4FZ36l0vbvBkefTTfHu7Jd+YwZUtdwgPC8+0xRdyWH445qsWAVjhO0S8+7/3JwDc4RlvvInun/QPOHrMkjlFRH2DlZe67E0chbQDswrHjQABQ7ezbIh5o/ZH1vDDjBdMxvI6KqlwsKQnuusuxb2o+6jBhKfN/i6F0aPGr31lE/mPHMcoK/42k+uIYSfV3wjHPlwr8BnTE8T+IHcdcWXBMEdBJEfFgQxcNZdbOWaZjeBUVVbnYgAGOK1AZUGP+VpaMTKZRZHUjjy9iedNxjFqewDFX9dzbNfmvpKbgGBVNxnE6P56sBXIEsPHsv1POHmMvju2s9uM4rX8KxwjphebiGEG95uzbJc8e6yCwBCiVt6cnYmV27PSc0pO4JG2vmF80R1Wy+vFHx2iqYckhATz4Wj0mJCwwHUXEWqYAO4AE/luZfz2O7aV2Al9d5uOewLFPKsBgHEW2No4R0knAvzjmpwbjWETVjP8WU51zCPgBx56tAWdvywB+x7H7QCEcC7sK5f7pibiD+2rex9edvzYdwyuoqMp/tm+HOnXg1CnTSTK9+VJzXvabix19m4qIiHX8dOdP3F71dtMxPJ6KqjikpUGjRrBsmekkF5l0/3V0L7+GxFTn7uUqIiKSW4WCC7HusXXEhMWYjuLRNEdVHN5915IlFeD2sYuYO6MUxUKKXv3OIiIi+eDYmWM8+MuDpmN4PI2oCmzcCLVrQ3Ky6SRXtL98UTr1KcjyuI1Xv7OIiEg++KT9JzxS7xHTMTyWiqq3y8iAJk1g0SLTSbIlMSKY7oOvZdIp98grIiKeLdQ/lJW9V1KhUAXTUTySTv17u2HD3KakAoScOsPEpxbxoq256SgiIiKcTj3Nff+7j/SMdNNRPJJGVL3Ztm1QsyacOWM6Sa5MeKQxDxZfTnK6tacsiIiI53vthtd4ufnLpmN4HBVVb2W3Q4sWMGeO6SR5srBtdW5tcZDDZ46ajiIiIl7Mz8ePRQ8uom6xuqajeBSd+vdWH3/s9iUVoNGf61jyVSA1IjQ3SEREzEnLSOO+/93HmVT3PEtpVSqq3mj3bnjuOdMpnKb0hn+Z/8YBOkTWNx1FRES82MajG3luuuf8/2oFKqre6OGHISHBdAqnCj+ewJSnlvGkvxZZiYiIOSOXjmTZfmvuS+6OVFS9zZgx8PffplO4hE+GnQ9enMPo49fj7+NvOo6IiHihDHsGj/32GBn2DNNRPIIWU3mT/fuhalWIizOdxOVm33ott1+3h+NJJ0xHERERL/RZh8/oVbeX6RhuT0XVm3TqBL/+ajpFvtlWqxQd7vNlc/xO01FERMTLFAwuyOa+mykcUth0FLemU//eYsIEryqpAOVX7WHRu8e4KbKO6SgiIuJljp85roVVTqARVW9w4gRUrAhHvXOv0TR/X554vQkfJ801HUVERLyIDRsLHlzAdSWuMx3FbWlE1RsMHuy1JRXALzWdUc/NZURCM3xtvqbjiIiIl7Bj57HfHtPlVfNAI6qebuNGx2VS09JMJ7GEaV3qcletrcSlnDIdRUREvMSIdiPo26Cv6RhuSUXV07VtC9OmmU5hKRsblKVDlzR2JOwxHUVERLxAZGAkWx7fQpHQIqajuB2d+vdkv/2mknoJVZbsYPGwBJpG1jQdRUREvEBcchzP/P2M6RhuSSOqnio1FapXhy1bTCexrJQgf3q/3pBxp+eZjiIiIl5gbs+5NC3d1HQMt6IRVU81YoRK6lUEJKUy9pl5vJvSHB+bfhRERMS1+vzeh7QMrRnJCY2oeqIjR6BCBa+4ApWzTOnegHsrbSAhNcF0FBER8WAftv6QAY0GmI7hNlRUPVGvXjB6tOkUbmd104p06nSaPaf/NR1FREQ8VHhAOJv6bqJYeDHTUdyCznd6mlWrYMwY0ync0rX/bGHJqGQaRlYzHUVERDxUfEo8T/31lOkYbkNF1dP07w8ZGaZTuK2iu44y+6Vt3B3e2HQUERHxUN+v+57F+xabjuEWVFQ9yU8/wZw5plO4vaDTyXz71AJezWiODZvpOCIi4oFenPmi6QhuQXNUPUVSElSpArt2mU7iUX58qBE9Y1dxJu2M6SgiIuJhZnafSYsyLUzHsDSNqHqK999XSXWBLl8sZM7sWK4JKWo6ioiIeBiNql6dRlQ9waFDUK4cnD5tOonH2lcxhk6PRrEybpPpKCIi4kGm3j2V9hXbm45hWRpR9QRDhqikuliJLQf559U9dI5oaDqKiIh4kJdmvYTGDC9PRdXdHToEn35qOoVXCD2ZyKSnFvOcb3PTUURExEOsOriKiRsmmo5hWSqq7m7IEDijhT75xWaHt1+ew7gjTQjwDTAdR0REPMCgWYNIz0g3HcOSNEfVnR08CGXLqqgaMq99DW5rdoAjZ46ajiIiIm5u7C1j6Vmrp+kYlqMRVXem0VSjrv9tLYu/CaJaRHnTUURExM29OudVUtJTTMewHBVVd3XwIHz2mekUXq/M2n0seOsQ7SLrmY4iIiJubNfJXYxePtp0DMtRUXVX77yj0VSLiDgaz6/PrKB/QDPTUURExI29+c+bnEnV/+3nU1F1RwcOaDTVYnzTMhj6wlw+O9kUPx8/03FERMQNHUg4wMglI03HsBQVVXc0ZIjjkqliOb2G/cO0VdUpEBhlOoqIiLihIfOHcCr5lOkYlqGi6m40mmp5LX9exaKfoqgQHms6ioiIuJljZ47x4cIPTcewDBVVd/POOxpNdQMVl+9i8XsnaBlZ23QUERFxMx8u/JBjicdMx7AEFVV3sn8/fP656RSSTQUOxjHt2TX0CmlqOoqIiLiR+JR4RiwZYTqGJaiouhONprodv9R0Pnv2H4YmNsPX5ms6joiIuIlRS0dpBwBUVN3H/v0wWvuruav+787ll021iQiIMB1FRETcwNHEo4xbNc50DONUVN3FsGEaTXVzN3+3jAW/RlMmrKTpKCIi4gY+XPQhGfYM0zGMUlF1B4mJ8MUXplOIE1RbuJ3FHyVyfWRN01FERMTith3fxuRNk03HMEpF1R2MHw8nTphOIU4SvfcYM57fSPewJqajiIiIxb234D3TEYxSUXUHw4ebTiBOFpCUyldPz+ft1ObYsJmOIyIiFrVo3yLm75lvOoYxKqpWN306bNhgOoW4yHNvzmHSzgaE+oeajiIiIhblzaOqKqpWp9FUj9f5q8X883cJSoReYzqKiIhYiL+PP03C7mXL6EFs3246jRk2u91uNx1CLmPHDqhQATK8e8WftzhQrgi39C3M0jiNoIuIeLMCQQWpmfIIm77uw6FtxQB44gnHBkDeRkXVyp58EoYONZ1C8tGZ8CB6Dq7Nj/ELTUcREZF8Via8EiX29WfZmO6cORWS5X3h4bBvH0R42XbcKqpWlZAAJUpAXJzpJJLP7DYY/EpzXmOO6SgiIpIP6kS2wmfJAJZ9387xn8BlDBvmGFn1JiqqVjVqFPTtazqFGPRdr0Y8UGolSWm60IOIiKcJ9A2kftA9HJrSn63zsre3drlysGUL+HjRCiMVVSuy26FqVdi0yXQSMWxx62rc2uoIBxMPm44iIiJOUDi4CNXOPMr6cY9ydFfRHH/8H39A27YuCGZRKqpWNG2ad30XyhXtrXwNHXuFs/rUFtNRREQkl8pHVCdmZ3+WjLmXlMTAXB+nc2f4+WcnBrM4FVUruvlmx59MImclFAil26Bq/BK3xHQUERHJJhs26kS2wT7/SVb8dJNTjunnB3v3QkyMUw5neSqqVrN1K1Sq5Dj9L3KeDB8bz73WjPfStMhKRMTKgv2CqRdwH//+1J8dS6o4/fhvvgkvvOD0w1qSiqrV9OsHI0aYTiEWNrbv9TxSdAkp6Smmo4iIyHmKhlxD5fg+rB3bm+P7CrvsccqUge3bweYFV+BWUbWSM2ccY/mnTplOIhY3t1NNbmu8j2NJx01HERHxepUia1F46wCWfNmV1KSAfHnMv/6Cm5wzm8DSVFSt5IcfoGtX0ynETeyoWZIOPf3ZeGqH6SgiIl7Hx+ZDvYgOJM8ZwOrJN+T7499xB0ycmO8Pm+9UVK2kQwf47TfTKcSNxEVHcNcLFZgWt9x0FBERrxDqH0odn57s/qE/e1aVN5bD399xpaoiRYxFyBcqqlZx5AgUKwZpaaaTiJtJ9/Oh/xvXMzJprukoIiIe65rQElQ88TirvnyYuIMFTMcB4J13YOBA0ylcS0XVKkaMcCykEsmlj59qxhORC0jL0B87IiLOUjWyPpEbB7Bk7J2kp/qZjpNFuXKwbZvpFK6lomoVDRrA0qWmU4ib+/uOOnSpu52TyXGmo4iIuC1fmy/1w2/l9PQBrP29iek4VzR/PjRubDqF66ioWsHmzVC5sukU4iE21S9Dx64ZbIvfbTqKiIhbCQ8IpzYPsuO7fuxbW8Z0nGx57DEYNcp0CtdRUbWCl15y7N4r4iTHr4ni9mdjmR23ynQUERHLKxEWS7kj/Vgx5kHij0aYjpMjhQvDgQOOK1Z5IhVV0+x2x869uzX6Jc6VGujHY6834ovEf0xHERGxpBqRjQlZM4Cl4zuTkeZrOk6uTZ0K7dubTuEaKqqmzZ0LzZubTiEe7MPnmvNM8D9k2DNMRxERMc7Px4/6oXcQ9+cANvzdwHQcp7j7bvj2W9MpXENF1bSHH4YvvjCdQjzc1G71uafaJuJT4k1HERExIjIwilrpD7Plm8c5sKmk6ThOFRoKhw45XnsaFVWTkpMdl0w9edJ0EvECa5tUoOOtiew+/a/pKCIi+aZ0eHlKH3iC5WN6cvpEmOk4LvPNN9Ctm+kUzqeiatJPP8Gdd5pOIV7kcOnC3No/hoVx60xHERFxqWsjmxOwYgDLJnTEnuFjOo7LtWsHv/9uOoXzqaiadMst8MsvplOIl0kOCeDB1+oxIWGB6SgiIk7l7+NPg5CuHJ06gM2za5uOk6/8/GD/foiONp3EuVRUTTl61HHJ1NRU00nES735cnNe9p2LHf0KEBH3VjCoEDWSe7Px674c3n6N6TjGjBwJffqYTuFcKqqmfPKJY5deEYN+euA6epRbQ2JqoukoIiI5Vja8MsX39WfpF91Jig82Hce4Fi1g5kzTKZxLRdWUNm3gr79MpxBhecvKdGp7kv2JB01HERHJljqRrbAtHsDyH9qB3WY6jmX4+TlW/xcsaDqJ86iompCQAIUKQUqK6SQiAOwvX5ROfQqyPG6j6SgiIpcU6BtI/aBuHJrcn63za5iOY1njxkGPHqZTOI/nL4Ozor/+UkkVSym27RBzX9nF7RHXmY4iIpJF4eAiNOcVwr/Yw7xnxqikXsXkyaYTOJdGVE144AEYO9Z0CpGL2G3w8uDmvGmfYzqKiHi58hHVidk5gCVjupGSGGg6jtsICXGs1w72kCm7Kqr5zW53bPJ/+LDpJCKXNeGRxjxYfDnJ6cmmo4iIF7Fho25kW9LnD2DlTzeZjuO2/vc/uPVW0ymcQ0U1vy1eDNfp9KpY38K21bm1xUEOnzlqOoqIeLhgv2Dq+Xdn30/92bm0suk4bq9HD8dcVU/gZzqA15k61XQCkWxp9Oc6luwpTseHKrD21FbTcUTEAxUNuYbKp/qwZvQj/PNvIdNxPMbUqZCeDr6+ppPknUZU81utWrB6tekUItkWXzCMe16uwtS4paajiIiHqBxZm0JbBrBk7F2kJgWYjuORZs507Kvq7rTqPz/t26eSKm4n/HgCU55axpP+zU1HERE35mPzoUFkJ65dOYtNA1Yw/5P7VFJdaMoU0wmcQyOq+enTT+HRR02nEMm1L/pdz2OFF5OaoUv/ikj2hPqHUtf3fnZ9/wR7VpU3HcdrVKwImzebTpF3Kqr5qX17+P130ylE8mT2rddy+3V7OJ50wnQUEbGwa0JLUOHE46wa04tTh6JMx/FKu3ZB6dKmU+SNimp+SUx0XI0qKcl0EpE821arFB3u82Vz/E7TUUTEYqpFNiBiwwCWjLuD9FSt2Tbp88/h4YdNp8gbzVHNLzNmqKSKxyi/ag+L3j1Gq8g6pqOIiAX42ny5LuJ2qi+Zx/oBi1k4uqtKqgX89ZfpBHmnEdX80qsXjB5tOoWIU6X5+/LE6034OGmu6SgiYkBEQAS1eZBt3/bj33WxpuPIBQoUcFylyseNhyVVVPNL8eKwf7/pFCIuMfLpZvQPn0+6Pd10FBHJByXCYil3pB8rxjxI/NEI03HkChYtgoYNTafIPTfu2G5k9WqVVPFofd+fy2/raxEZoP+wRDxZjcgmXLf7J/Y/t405QwaopLoBdz/9r6KaH2bNMp1AxOXa/LichZMLUzaslOkoIuJEfj5+NArvSpV5S1g7YB6Lxt5ORpoHXPLIS7h7UdWp//zQuTNMnmw6hUi+OFqiILc9VYJ/4taYjiIieRAZGEWt9F5s+aYvBzaVNB1HcsnPD44fh/Bw00lyR0XV1ex2iI6GY8dMJxHJNylB/vR+vSHjTs8zHUVEcig2vAKl9j/BsjE9STwZajqOOMGUKdCpk+kUuaNT/662bp1KqnidgKRUxj4zj3dTmuNj068ZEXdQK/IGGmyfwu5nNjH3gz4qqR5kzhzTCXJPm5y5mjt/d4jk0TNvzaFi9wZ0q7Se06mnTccRkQv4+/jTIKQrR6cOYNXs2qbjiIvMc+OTWzr172pdusDEiaZTiBi1umlFOnZKYO9p7X4hYgUFgwpRI/kRNn7dh8PbrzEdR1zM3x9OnoSQENNJck5F1dViYuDQIdMpRIw7WCaaW/sVYXHcetNRRLxW2fDKFNvbn2VjupMUH2w6juSjmTOhRQvTKXJOk8dcafNmlVSRs2J2HmH2S9voGt7IdBQRr1Mn8ibqbfqdHU9vYN6w3iqpXmj+fNMJckdzVF3JXb8rRFwk6HQy3z21kCqDmjPYZy52dEJHxFUCfQOpH3gvByf3Z8WC6qbjiGHuOk9Vp/5d6eGH4YsvTKcQsaQfH2pEz9hVnEk7YzqKiEcpHFyEaomPsX7coxzdXcR0HLGIiAg4cQJ83OxcuoqqK1WrBhs2mE4hYllLW1XhltbHOZCoKTIieVU+ojoxOwewZEw3UhIDTccRC1q5EmrVMp0iZ1RUXeXkSShY0LHhv4hc1r6KMXR6NIqVcZtMRxFxOzZs1I1sR/q8Aayc1Mp0HLG4ESOgb1/TKXLGzQaA3ciiRSqpItlQYstB/nl1D50jGpqOIuI2gv2CaRrcm9jfNrBswG8qqZIt7rh0RkXVVRYsMJ1AxG2Enkxk0lOLec63uekoIpYWE1KM5mlvEvTJXv4Z+Ck7l1Y2HUncyOLFphPknE79u8pNN8H06aZTiLidr/o0oVfMUlLSU0xHEbGMypF1KLh5AEvG3kVasr/pOOLGTpyAqCjTKbJPRdUV7HbHd8GpU6aTiLilee1r0Lnpfo4mHTMdRcQYH5sP9cI7kjxnAKun6GyDOMeMGdCypekU2adT/66wa5dKqkgeXP/bWpZMCKZaRHnTUUTyXah/KM2C+lLif5tZ8uRklVRxqpUrTSfIGRVVV1i71nQCEbdXZu0+Frx1iHaR9UxHEckXxUJL0jzlXXyH72PucyPYs0p/qInzrVhhOkHOqKi6goqqiFNEHI3n12dW0D+gmekoIi5TLbIBjf79jkMv7GDOW89w6lCU6UjiwdytqGqOqivcfTd8/73pFCIe5fP+TelTcCFpGWmmo4jkma/Nl/rhnUn4ewDr/mhsOo54ER8fx+zE0FDTSbJHRdUVqleH9etNpxDxODNvq8Ud9XdxIvmk6SgiuRIREEFt+0Ns++5x/l0XazqOeKn586Gxm/x9pKLqbCkpEBYGqammk4h4pC11Y+lwD2yN32U6iki2lQwrQ5kj/Vgx+kESjoebjiNezp2uUOVnOoDH2bxZJVXEhSou38XifyO5Y2BtZsa52fJV8To1IpsQsnoAS7+5lb1pvqbjiADuNU9Vi6mcTQupRFyuwME4pj27hl4hTU1HEbmIn48fjcPvpsq8JawdMI/F424nQyVVLGT1atMJsk8jqs6moiqSL/xS0/ns2X+o8mwzng6dT7o93XQk8XKRgVHUSuvF5vGPs2BLCdNxRC5r82bHtYlsNtNJrk5zVJ2tY0eYOtV0ChGv8vvd9bi7xhZOpehCG5L/YsMrUGr/Eywb05PEk26ylFq83u7dUKqU6RRXp6LqbLGxjq++iOSr9Y3K0eH2ZHYl7DMdRbxErcgb8F/+JMu+bY89QzPpxL1MmwatW5tOcXX6yXKm+HiVVBFDqi3czpKPztAksobpKOLBAnwDaBLWnYqzVrJqwCyWftNRJVXc0qZNphNkj366nGndOtMJRLxa9N5jzHhxM93DmpiOIh6mYFAhmtteJGrcLuY//RVb5tQyHUkkT9ylqGoxlTNpIZWIcYGJKXz19HyqvNicF/znYkezmyT3ykZUofie/iwdcx9z4oNNxxFxGhVVb6SiKmIZz705h0o9GnJfxXWcTj1tOo64mTqRN2Fb9CTLf2zDDrsbLI0WySF3KapaTOVMLVvCrFmmU4jIeVbeUIlO7U+x7/QB01HE4oL8gqgX0I2Dk/uzbUF103FEXC4uDiIiTKe4MhVVZ9KKfxFLOlCuCLf0LczSuA2mo4gFRQcXperpx1g39lGO7Y02HUck3yxaBA0bmk5xZSqqzpKRAYGBkJZmOomIXMKZ8CB6Dq7Nj/ELTUcRi6gQUYOiOwaw5Mt7SEkMNB1HJN999RV07246xZVpjqqzHDigkipiYcHxSXz/9EIqv9Kc15hjOo4YYsNG3ch2pM97kpWTbmSr6UAiBu3aZTrB1amoOsvevaYTiMhV2Ozw6uA5VO7ViAdKrSQpLcl0JMknIf4h1PXrzr6fnmDZ0sqm44hYgjvMVlRRdRYVVRG3cffnCynTphq3tjzMoTNHTMcRF4oJKUalU31Y/dkj/HOgoOk4IpayZ4/pBFenouos7vDVFpFM101bz5I9xej4cEXWnNpiOo44WeXIOhTcPIAlY+/iYLK/6TgiluQO1UVF1Vk0oiridkpt3M/810O5Z1ADfo1bYjqO5JGPzYd64R1Jmv0ka35pZjqOiOW5Q1HVJVSdxR2+2iJykbATp5n81FKe8WtuOorkUph/GM0CH6fEz1tY8uRklVSRbEpKgsOHTae4Mm1P5Sz168OyZaZTiEgejO17PY8UXUJKeorpKJINxUJLUv7446z68mFOHYoyHUfELS1dCvXqmU5xeSqqzhITA4cOmU4hInk0t1NNbmu8j2NJx01HkcuoFtmQiPUDWPLV7aSnagabSF5MmgS33WY6xeXpJ9wZkpOtP3YuItnS7Jc1LNlVkg49o9h4aofpOHKWr82X+mGdSfj7Sdb92ch0HBGPYfUtqjRH1Rn27QMNTIt4jLJr9rLw7aO0iaxrOorXiwiIoLn/k8RM3MaipyaqpIo4mdWX2KioOoPVv8oikmORR07x2zMr6RukhTkmlAwrQ7PEYWS8v485L37Av+tiTUcS8Uj795tOcGU69e8M2ppKxCP5pmUw4rm5VHmqGU9ELiAtQ5dJdrWakdcTvHoAS76+lb0ZGksRcbUjFr/miYqqM6ioini0xz6YS4U76tCl7nZOJseZjuNx/Hz8qB9yJyf/eJI1Myy8/FjEAx09ajrBlamoOoNO/Yt4vJt+WsHC3WXo2DWKbfEWX33gJqICC3BtWi82j+/Lwi0lTMcR8UoaUfUGVv8qi4hTVF66k8X7orjt2WuZE7fadBy3FRtegZL7n2D5sJ7MORlqOo6IV7P6iKr2UXWGG2+EmTNNpxCRfJIa6Mejr1/HmMR5pqO4lVqRLfBfPoClEzqA3WY6joicdeIEREWZTnFpmqnuDHGasybiTfyT0/ji2Xm8n9QMH5t+jV5JgG8ATUJ7UHHWSlYNmMnSbzqqpIpYjJVPDGtE1RkqVoStW02nEBEDpnarzz3VNhGfEm86iqUUCipM9eRH2DCuD0d2xpiOIyJXMH8+NG5sOsWlaY6qM5w6ZTqBiBjSYcJS5jepQMdbI9h9+l/TcYwrF1GVYrv7s/TLe5kTH2w6johkg5XnqaqoOoNO/Yt4tRrzt7JkX2Fu7V+dhXHrTMcxom5ka+yLBrDixzZs16l9Ebdi5VP/mlyVV6mpkJRkOoWIGFZk91FmvbiFbmEWPX/mAkF+QVwf8iDlpq1j+YBprPihreafirghK4+oqqjmlU77i8hZgYkpfPP0At5Ib44Nzy1s0cFFaZ7xKqGf7WHes1+wfWE105FEJA8SEkwnuDyd+s8rnfYXkQu8+PocKj1wHT3KrSExNdF0HKepGFGT6B0DWPrl3cxJDDQdR0Sc5PRp0wkuT0U1rzSiKiKXcMeXiyjTsjKd2p5kf+JB03FyzYaNepE3kzZvACsn3cgW04FExOlUVD2ZiqqIXEbdmZtYsjeGTo9VZkXcJtNxciTEP4S6fj3YN/EJli6rZDqOiLiQlYuq5qjmlU79i8gVFN96kH9e2c3tEdeZjpItMSHFaJ72FgEj9/LPwI/ZqZIq4vGsPEdVRTWvNKIqIlcRcuoME59axAu25qajXFblyDo0Pjieoy/vYs4bz3PyQEHTkUQkn1h5RFWn/vNKRVVEssFmhzdfmUOVRxrzUPHlJKcnm46Ej82H+uGdODN7AGt+aWY6jogYoqLqyXTqX0Ry4N5PF1CubXVubXGQw2fMbF4Y5h9GHZ8H2PV9PxavLmckg4hYh4qqJ9OIqojkUKM/17FkT3E6PlSBtae25tvjFg8tRfnjj7Ny1MPMPRKZb48rItamourJrPzVFRHLKr3hX+a/EcY9L9dnatxSlz5W9cjrCF8/gCVf3ca/qfq1LyJZWbnKaDFVXmVkmE4gIm4q/HgCU55axpP+zl9k5Wvz5bqIO6m2aAHrBixk4RddSFdJFZFLSDY/Zf6y9Fsrr2yee5lEEXE9nww7H7w4hyr9ruexwotJzUjN0/EiAiKoZX+I7RP6sWhDaSelFBFPZuUxNxVVERELeGj4PMrfei23X7eH40kncvzxpcLKEnu4HyuGP8Dc4+EuSCginspuN53g8lRU80ojqiLiJDdMXs3iXaXocF8Um+N3ZutjakZeT/CqJ1ky/hb2ZGg2l4jknEZUPZmKqog4UflVe1h0III7n6vD9LgVl7yPv48/9UPv5MTvA1gzo14+JxQRT2Ploqo/v0VELCbq0Cn+eHY1jwY3zXp7YAGa+z5HofE7WfDUBDaqpIqIE+jUvyfTiKqIuIBfajofD/yHKk835aNiRyn+b1+WDevBnJOhpqOJiIex8oiqiqqIiIXdO3ovz6XuYHui/igWEdew8oiqTv3nlUZURcSFxtQaQaJKqoi4kJVHVFVURUQsKsPmy8c72piOISIeTkXVk2lEVURc5Ld6r7Bzr7/pGCIixqioiohY1Ij0x0xHEBEvEBhoOsHlqajmlUZURcQFNpVpx/SVBU3HEBEvEBRkOsHlqaiKiFjQqJLvYLfrD2ERcT0VVU+mEVURcbL4sGv4amUN0zFExEuoqHoyFVURcbJxtT8iPl6/W0Qkf6ioerKQENMJRMSD2LExal8n0zFExIuoqHqyqCjTCUTEg/xdZyCbd1p4Ca6IeBwVVU8WGWk6gYh4kBG+T5iOICJeRkXVk2lEVUScZGeJpvy+vKjpGCLiZVRUPZlGVEXESUaV/YCMDC2iEpH8paLqyTSiKiJOkBhciC/X1DUdQ0S8UFiY6QSXp6KaVxpRFREnmFDvQ06c1K9kEcl/BS18ETz9VswrjaiKiBOMPHSn6Qgi4qVUVD2ZRlRFJI/m1uzLmi3BpmOIiJcqUMB0gstTUc0rPz8IDTWdQkTc2IiQgaYjiIgX04iqp9Ooqojk0r6YekxeVtx0DBHxYhpR9XSapyoiufRppaGkpWlLKhExRyOqnk4jqiKSC8kB4Xy+vrHpGCLi5TSi6uk0oioiufBDvfc4clS/hkXELI2oejqNqIpILow82c10BBERjah6PI2oikgOLa56P0s3WPhyMCLiFSIiHBsYWZWKqjMULmw6gYi4mZEFXjIdQUSEYsVMJ7gyFVVnKFXKdAIRcSOHClfjx6VlTMcQEaG4xXfHU1F1htKlTScQETfyebWPSEnRllQiYp6KqjfQiKqIZFOabyCfbb7BdAwREQBKlDCd4MpUVJ1BRVVEsunnBm/z70Ff0zFERACNqHqHkBAtqBKRbBlx+gHTEUREMqmoegvNUxWRq1hd8U7mrdG+yyJiHSqq3kKn/0XkKkYUed10BBGRLFRUvYVGVEXkCo5HleXb5RVNxxARyeTnB0WLmk5xZSqqzqKiaszbQH0gHCgC3ApsvuA+24HOQDQQAXQBDl3luLGA7RIvfc67z5NAQaAkMOGCj58IdMzJExGPNuba4Zw5oy2pRMQ6rrkGfCzeBC0ez43o1L8xc3CUx0XA30Aq0Bo4ffb9p8++bQNmAvOBFBwlMuMKx10KHDjv5e+zt9959vWvwLfAX8C7wEPA0bPviwNeBEbl6ZmJp8iw+fLx9tamY4iIZBEbazrB1Vn46q5uRiOqxvx5wdvjcIysLgea4Simu4CVOEZTAb4CCuAorq0uc9zoC95+BygHND/79kbgBqDe2Zf+wE6gMPAs8CigP18E4Nd6r7Jrqb/pGCIiWVSoYDrB1WlE1Vk0omoZcWdfFzz7OhnHaGrgefcJwvHNPy+bx0wBvgEeOHssgGuBZcAJHKX4DFD+7DFXAP1yF1880Mj0R0xHEBG5iIqqN4mOduynKkZl4BjZbAJUP3vbdUAoMBBIxDEV4GkgHccp/eyYDJwEep53WxvgXhzzY3viGKUNxTGS+inwCVDpbJb1uXgu4hk2lWnH9BWFTMcQEbmIiqq3KVnSdAKv1wdYB3x/3m3ROBY2/QqEAZE4Smcdsv8DMAZoBxS74PbBwDZgLY7FWm/jmErgD7yBY3T1IaB7Tp+IeIyRJd8xHUFE5JLcoahqjqozlS4Nmy9cby75pS8wFZgLXHjp4tY4Vv4fxfFNHwXEAGWzcdzdwHTg56vcbxOO6QErgS9xzI+NxrHDwANAPI6dCcR7nAovzlcrapiOISJyEZsNypc3neLqNKLqTGXKmE7glew4Sur/cCyOutJXoTCOkjoTOAx0ysbxx+JYnNX+Khl6Ax/iGLVNx7H7AOe9Ts/GY4lnGVd7GAkJ2pJKRKznmmvcY8aiiqozVatmOoFX6oNjJPNbHCOWB8++nDnvPmNxbF+1/ex97wQG4JhDes6NwMgLjp1x9mN7cOXTD1/gGD09t29qExxleBEwFKiKoyCL97BjY9Se7PwpJCKS/9zhtD/o1L9z1dApPhM+Ofv6hgtuH8t/i582A88Dx3Fs5P8ijqJ6vnNTA843HdiD49T95RwC3gQWnHdbA+ApHKOwRXAstBLv8lfd59myPMB0DBGRS3KXomqz2+120yE8xvHjUEire0UEOtQ7yG/LLH5tQhHxWkOGwLPPmk5xdTr170wFC0Lx4qZTiIhhO0o2548VRUzHEBG5rIoVTSfIHhVVZ6tZ03QCETFsVNn3ycjQIioRsS53ma2ooupsKqoiXi0xuBBfrqpjOoaIyGWFhkLZ7OzPaAEqqs6moiri1b6pN4yTcfrVKiLWVa2aYx9Vd6Dfps7mLmPpIuISIw/ebjqCiMgVuVNVUVF1tsqVIUBb0oh4oznX9mPt1mDTMURErkhF1Zv5+zvKqoh4nRHBz5iOICJyVbVqmU6QfSqqrqB5qiJeZ+81DZi8VNvTiYj1qah6OxVVEa/zacUPSU93k9UJIuK1YmMhMtJ0iuxTUXUFd5r8ISJ5lhwQzuj1jUzHEBG5qtq1TSfIGRVVV9CIqohX+b7++xw5ql+nImJ97nTaH1RUXaNYMShc2HQKEcknI493Mx1BRCRbGjQwnSBnVFRdRaOqIl5hUbUHWbYx1HQMEZGrstnguutMp8gZFVVXadjQdAIRyQcjIl80HUFEJFsqV4aoKNMpckZF1VWaNDGdQERc7FDhavy0LNZ0DBGRbGnkhms+VVRdpVEj97mQrojkymfVhpOSop9zEXEPKqryn4IFdYUqEQ+W6hfMp5uam44hIpJt7jY/FVRUXUun/0U81s/13+bAIV/TMUREsiUiAqpWNZ0i51RUXUlFVcRjjUjoaTqCiEi2NWwIPm7Y+twwshtp3Nh0AhFxgVUVuzB/rRtdg1BEvJ47zk8FFVXXqlgRoqNNpxARJxtR5HXTEUREckRF1SJiY2MZNmxY5tsHDx7kpptuIjQ0lCgTm4c1bZr/jykiLnMsqhzfLqtgOoaISLb5+npJUe3Zsyc2m4133nkny+2TJ0/Gls9bMY0bN+6SxXPp0qX06tUr8+2hQ4dy4MABVq1axZYtW/Ix4VktWuT/Y4qIy4y5djhJSdqSSkTcR926EOmms5VyPKIaFBTEkCFDOHHihCvy5Fl0dDQhISGZb2/fvp26detSoUIFihQpkv+Bbrgh/x9TRFwi3cefj7e1Nh1DRCRHbrzRdILcy3FRbdWqFTExMbz99tuXvc+8efNo2rQpwcHBlCxZkn79+nH69OnM9x84cID27dsTHBxMmTJl+Pbbby86Zf/hhx9So0YNQkNDKVmyJI899hgJCQkAzJ49m/vvv5+4uDhsNhs2m43BgwcDWU/9x8bGMmnSJL7++mtsNhs9e/bM6dPNu2rVNE9VxEP8Wu9Vdv/rZzqGiEiOtGxpOkHu5bio+vr68tZbbzFixAj27dt30fu3b99O27Ztuf3221mzZg0//PAD8+bNo2/fvpn36d69O/v372f27NlMmjSJzz//nMOHD2cN5uPD8OHDWb9+PV999RUzZ87k2WefBaBx48YMGzaMiIgIDhw4wIEDB3j66acvyrJ06VLatm1Lly5dOHDgAB999FFOn27e2WzQrFn+P66ION3I1N6mI4iI5EhgoHvvlpmrxVSdO3emVq1avPLKKxe97+2336Zbt27079+fChUq0LhxY4YPH87XX39NUlISmzZtYvr06YwePZqGDRtSp04dvvjiC86cOZPlOP3796dFixbExsbSsmVL3njjDX788UcAAgICiIyMxGazERMTQ0xMDGFhYRdliY6OJjAwkODgYGJiYog0NUFDp/9F3N6Gch2ZsbKg6RgiIjnSuDEEB5tOkXu5Poc1ZMgQWrZsedFI5urVq1mzZg0TJkzIvM1ut5ORkcHOnTvZsmULfn5+1KlTJ/P95cuXp0CBAlmOM336dN5++202bdrEqVOnSEtLIykpicTExCxzUN2CiqqI2xtZ7C3YbjqFiEjOuPP8VMjD9lTNmjWjTZs2PP/881luT0hIoHfv3qxatSrzZfXq1WzdupVy5cpl69i7du2iQ4cO1KxZk0mTJrF8+XJGjRoFQEpKSm4jm6N5qiJu7VR4ccavrGY6hohIjrl7Uc3TqoB33nmHWrVqUalSpczb6tSpw4YNGyhfvvwlP6ZSpUqkpaWxcuVK6tatC8C2bduy7CKwfPlyMjIy+OCDD/A5e72vc6f9zwkICCA9PT0v8fOPzQZt28L48aaTiEgujK09nIS52pJKRNxLRATUr286Rd7kacP/GjVq0K1bN4YPH55528CBA1mwYAF9+/Zl1apVbN26lSlTpmQupqpcuTKtWrWiV69eLFmyhJUrV9KrVy+Cg4Mz92ItX748qampjBgxgh07djB+/Hg+/fTTLI8dGxtLQkICM2bM4OjRoyQmJublqbhep06mE4hILtixMWpPB9MxRERyrHlzx2b/7izPV6Z67bXXyMjIyHy7Zs2azJkzhy1bttC0aVNq167NoEGDKFasWOZ9vv76a4oWLUqzZs3o3LkzDz/8MOHh4QQFBQFw7bXX8uGHHzJkyBCqV6/OhAkTLtoOq3HjxjzyyCPcddddREdH8+677+b1qbhWmzYQEGA6hYjk0LS6L7B1l352RcT93HST6QR5Z7Pb7XbTIfbt20fJkiWZPn06N7r7ZIoradMG/vrLdAoRyYH29Q7x+zIDFwsREcmjHTugTBnTKfLGyM7VM2fOJCEhgRo1anDgwAGeffZZYmNjaebp+4127KiiKuJGtpdqwZ8rtBBSRNxP9eruX1LBCaf+cyM1NZUXXniBatWq0blzZ6Kjo5k9ezb+/v4m4uQfzVMVcSujyrxHRoYWUYmI++nY0XQC57DEqX+vUqsWrF5tOoWIXMXpkGhK+B/kZJyRv+dFRPJkwQJo1Mh0irzTb+D85il/4oh4uG/qDlVJFRG3VKQINGxoOoVz6LdwftPpfxG3MPLA7aYjiIjkSvv24OMhDc9DnoYbqVcPrrnGdAoRuYJZ1/Zn3bYg0zFERHLFk07eqqjmN5sNOmjzcBErGxn0tOkIIiK5EhgIrVubTuE8Kqom6PS/iGXtKXYdU5YVu/odRUQsqEULCA01ncJ5VFRNuPFGCAkxnUJELuGTCh+Qnq4tqUTEPd1yi+kEzqXtqUy55Rb45RfTKUTkPEmBkZQMO87RY/obXkTcj68vHDgA0R50nRL9NjbFk2Y6i3iI7+u9r5IqIm6rZUvPKqmgompOx46OP31ExDJGHr/bdAQRkVzr2tV0AudTUTWlaFHHXFURsYSF1R9m+UYPWoEgIl4lIABuu810CudTUTWpe3fTCUTkrBHhL5iOICKSa23aQFSU6RTOp6JqUufOEB5uOoWI1zsYXYOflpU2HUNEJNc88bQ/qKiaFRICt+syjSKmfVb1I1JTtSWViLin4GDP3aJdRdU0nf4XMSrVL5jPNjUzHUNEJNfat4ewMNMpXENF1bQbboDSOuUoYsqk+u9w4JB24BAR9+Wpp/1BRdU8mw3uvdd0ChGvNSKhp+kIIiK5FhnpGFH1VCqqVqDT/yJGrKh8DwvWRpiOISKSa3ffDUFBplO4joqqFVSsCA0bmk4h4nVGFn7FdAQRkTx58EHTCVxLRdUqNKoqkq+OFSjPd8sqmI4hIpJrNWtCvXqmU7iWiqpVdO3quKyEiOSLL2oOJylJW1KJiPt64AHTCVzPZrfb7aZDyFm33w4//2w6hYjHS/fxp+w1iez51890FBGRXAkIgP37oVAh00lcSyOqVqLT/yL54pd6r6mkiohbu+UWzy+poKJqLTffDIULm04h4vFGpvYyHUFEJE88fRHVOSqqVuLv79m79opYwPpynZi5sqDpGCIiuVayJNx0k+kU+UNF1Wp69zadQMSjjSz2lukIIiJ50rMn+HhJg9NiKitq1QpmzDCdQsTjxEWUpHj6bk6f1mp/EXFPvr6wfbv3XH3dS/q4m3niCdMJRDzS2FofqaSKiFvr1Ml7SipoRNWa7HbH1aq2bTOdRMRj2LFRsXQS23Zrv2IRcV8zZ0KLFqZT5B+NqFqRzQaPP246hYhH+bPeSyqpIuLWatTwrpIKKqrWdf/9EBFhOoWIxxhBX9MRRETyxBvHsFRUrSo83DuujSaSD7aVasmfy6NNxxARybWCBaFbN9Mp8p+KqpX16+c9+0+IuNCo2Pew27WISkTc14MPQkiI6RT5Ty3IysqUcSzvE5FcOx0SzdhVtU3HEI/yCVATiDj70gj447z3HwTuA2KAUKAOMCkbxx0FxAJBQENgyQXvfxIoCJQEJlzwvolAxxw8B3Envr7Qp4/pFGaoqFqdtqoSyZPxdYcRd0qjqeJMJYB3gOXAMqAlcAuw/uz7uwObgV+AtcBtQBdg5RWO+QOOIvoKsAK4FmgDHD77/l+Bb4G/gHeBh4CjZ98XB7yIo+iKJ+rY0bu2pDqftqdyB7VqwerVplOIuKXq5c+wfluQ6Rji8QoC7wEPAmE4Rl3vO+/9hYAhOArmpTQE6gMjz76dgWPk9HHgORzldAXw/dn3FwWmnv2Y3kBlYIBznopYjrdtSXU+jai6A42qiuTKzFpPqqSKi6XjKI+ncUwBAGiMY4T0OI7C+T2QBNxwmWOk4BidbXXebT5n31549u1rcYzenjh73zNAeWAejgLbzxlPRiyoXj3vLamgouoe7rkHihQxnULE7YwMfMp0BPFYa3GMnAYCjwD/A6qefd+PQCqOUdRAHCOe/8NRLC/lKI7CW/SC24vimO8KjmkA9+IYQe0JfIVj/uujwKc4RnArAU34bwqCeILnnzedwCwVVXcQGAi9e5tOIeJW9hS7jl+WXWM6hnisSsAqYDGOstgD2HD2fS8DJ4HpOEZBn8QxR3VtHh9zMLDt7HE6A2/jGHX1B97AMbr6EI45suIJKleGzp1NpzBLc1TdxcGDjpnUKSmmk4i4heeaL2DInEZXv6OIU7QCygHP4hg5XQdUu+D95XGMfl4oBQgBfgJuPe/2HjgK75RLfMwmHKv8VwJf4iipP+KYghAGnALCc/lcxCrGjoWePU2nMEsjqu4iJgZ69DCdQsQtJAVG8sXahqZjiFfJAJKBxLNvX/jfq+/Z+1xKAFAXmHHB8Wbw37zX89lxTCf4EEcpTccx1YDzXqfnILtYUalS3rnB/4VUVN3Jiy9CgK5VLnI139X/kGPH9etNXOV5YC6wC8dp+OeB2UA3HKvvy+MokkuA7cAHwN9kHS29kf9W+INjesBoHHNPN+KYTnAauP8Sj/8FEM1/+6Y2AWYCi4ChOObKRuX+6YklPP00+PubTmGen+kAkgOlSzsuq/rppU4dicg5I452NR1BPNphHPNADwCRODb/nwbcdPb9v+PYUqojkICjuH4F3HzeMbbz3z6oAHcBR4BBOBZQ1QL+5OIFVoeAN4EF593WAHgKaA8UOftY4s6io+Ghy+1k5mU0R9Xd7NsH5ctDcrLpJCKWtKB6L5qs+8x0DBGRXHvjDcdJVNGpf/dTooT+zBK5ghHhL5iOICKSaxER3nu51EtRUXVHL7wAQdrEXORCB4pcy6RlpUzHEBHJtT59ICrKdArrUFF1R8WKaV9VkUv4rMowUlNtpmOIiORKZCQ884zpFNaiouqunnsOgoNNpxCxjFS/YD7b2NR0DBGRXHv6aShQwHQKa1FRdVcxMfDoo6ZTiFjGT/WHcPCwr+kYIiK5Eh0N/fubTmE9KqrubOBACA01nULEEkbE9zQdQUQk1557DsLCTKewHhVVd1akiJYGigDLK3dj4TpdLlJE3FPx4vDYY6ZTWJOKqrt79ln9CSZeb2ThV0xHEBHJtZdf1mY+l6Oi6u4KFYLHHzedQsSYowUr8v2y8qZjiIjkStmyjotOyqWpqHqCp5927BAs4oVG1/iIpCRtSSUi7mnwYPD3N53CulRUPUHBgloqKF4p3cefT7e1Mh1DRCRXqlWDbt1Mp7A2FVVP8eyzjtnYIl5kSv032POvn+kYIiK58v774KMmdkX69HiK0FAYMsR0CpF8NSL5YdMRRERypW1bx4tcmc1ut9tNhxAnatIEFiwwnULE5daVv5Ua2/5nOoaISI75+cGaNVCliukk1qcRVU8zfLjOI4hXGHnNG6YjiIjkSu/eKqnZpRFVT/TQQzBmjOkUIi5zMqIUJdJ3cfq0VvuLiHuJioJt2xy7S8rVaejNE731FkRGmk4h4jJja3+kkioibmnQIJXUnNCIqqf68EN46inTKUSczo6NCqWS2b5HGw+KiHupUAHWr9e+qTmhEVVP9fjjULmy6RQiTvdHvZdVUkXELb3/vkpqTqmoeip/fxg61HQKEacbQV/TEUREcuzGG6FTJ9Mp3I9O/Xu6jh1h6lTTKUScYmvpVlTa8xd2u+anioj7CAiA1at1ojM3NKLq6YYOdfyEiHiAUaXfVUkVEbfz7LMqqbmlEVVvMHAgvPuu6RQieZIQWpTivgc4dUpFVUTcR7lysG4dBAWZTuKeNKLqDV56CWJiTKcQyZOv6wxTSRURtzNqlEpqXqioeoPwcBgyxHQKkTwZtf9W0xFERHKkSxdo08Z0Cvemouotund3LDkUcUMzaj3Fhu0akhAR9xERAcOGmU7h/lRUvcmYMRAWZjqFSI6NDHzSdAQRkRx54w245hrTKdyfFlN5m08+gcceM51CJNt2F29MuYPzSE/X/FQRcQ9168KSJeCj4cA806fQ2zzyCLRoYTqFSLZ9XP4DlVQRcRu+vvDZZyqpzqJPo7ex2RxTAEJDTScRuaqkwEjGrG1gOoaISLY9+6xjRFWcQ0XVG5UpA++8YzqFyFV9W38ox47r15SIuIfq1WHwYNMpPIvmqHoru90xBWDOHNNJRC6rTuXTrNwUYjqGiMhV+fnBokUaTXU2DVV4q3NTAEJUAsSa5td4RCVVRNzGc8+ppLqCiqo3K1cO3nrLdAqRSxoR9rzpCCIi2VKzJgwaZDqFZ9Kpf29nt0Pz5vDPP6aTiGTaX7Q2sceXk5qq1f4iYm3+/rB4MdSubTqJZ9KIqrez2eDLLyE42HQSkUyfVR6qkioibuGFF1RSXUkjquIwdCg8qav/iHkp/qGUiorj0BFf01FERK6odm3HaKq/v+kknksjquLwxBPQuLHpFCJMrDdEJVVELC8oCL76SiXV1VRUxcHHB8aO1YUAxLiR8d1NRxARuar33oMaNUyn8HwqqvKfihXh449NpxAvtqzKfSxaF246hojIFXXqBH37mk7hHVRUJavu3aFnT9MpxEuNLKj9XUTE2ooXd6xBlvyhxVRyscREaNAA1q83nUS8yJFClSmZsIHkZK32FxFr8vGBGTPghhtMJ/EeGlGVi4WEwMSJmq8q+Wp09WEqqSJiac89p5Ka3zSiKpc3frxjKoCIi6X7+FMmJpG9+/1MRxERuaRGjWDuXPDTr6l8pRFVubz77oP77zedQrzA5AZvqqSKiGVFRsK336qkmqCiKlc2ahRUr246hXi4EWceNh1BROSyPvsMYmNNp/BOKqpyZcHB8OOPmq8qLrO2wm3MWR1lOoaIyCX17Qt33WU6hfdSUZWrq1IFPvnEdArxUCNj3jAdQUTkkpo0gQ8/NJ3Cu2kxlWTfgw9q8zhxqpMRpSietovERK32FxFriYmBFSvgmmtMJ/FuGlGV7Bs5UvNVxam+rD1cJVVELMfPzzHrTSXVPBVVyb7gYO2vKk6TYfPl4503m44hInKR99+Hpk1NpxBQUZWcqlzZsfxRJI/+qPsS2/f4m44hIpLF3XfDE0+YTiHnqKhKznXrBk8+aTqFuLkR9r6mI4iIZFGjBnzxhekUcj4tppLcyciADh3gjz9MJxE3tCW2NZV3/4ndrvmpImINkZGwbBmUL286iZxPI6qSOz4+8P33ULWq6STihkaVGqKSKiKW4esL332nkmpFGlGVvNm+HRo2hGPHTCcRN5EQWpTivgc4dUpFVUSsYfhwePxx0ynkUjSiKnlTrhz89BP4a1GMZM9XdT5SSRURy+jTRyXVyjSiKs7x+efQu7fpFOIGqpZNYuOOQNMxRERo2xamTnWc+hdr0oiqOEevXtC/v+kUYnHTaz+jkioillCtGvzwg0qq1amoivN88AHceqvpFGJhI/wHmI4gIkKRIo6R1IgI00nkanTqX5wrMRFuuAGWLjWdRCxmV/EmlDvwDxkZmp8qIuYEBcGsWXDddaaTSHZoRFWcKyQEfv0VYmNNJxGL+bj8hyqpImKUzQZjx6qkuhONqIprbNgATZrAyZOmk4gFnAkqQIngoxw/ob+NRcSct9+G554znUJyQv9riGtUrQqTJmnbKgHg23ofqqSKiFEDBqikuiONqIprjR8PPXqAvs28Wu1Kp1m1OcR0DBHxUvfeC19/7Tj1L+5FQxziWvfdB6NGmU4hBs2r8ahKqogYc/PNjnmpKqnuSUVVXO/RR+Hdd02nEENGhOlcm4iY0agRTJwIfn6mk0hu6dS/5J9Bg+D1102nkHy0v2htSh9bTlqahjJEJH9Vqwb//AMFCphOInmhEVXJP6+9Bk8+aTqF5KNPKw9TSRWRfFeqFEybppLqCTSiKvnvkUfgs89MpxAXS/EPpVRUHIeO6PqEIpJ/CheGefOgUiXTScQZNKIq+e/jjx1LMMWj/Vj/PZVUEclXUVHw118qqZ5EI6piRno6dOkCP/9sOom4yHXV4lm8Psx0DBHxEhER8Pff0KCB6STiTBpRFTN8feG776BtW9NJxAWWVumukioi+SYsDH7/XSXVE6moijkBAY4R1WbNTCcRJxtRYJDpCCLiJYKD4ddfHVftFs+jU/9iXnw8tGoFS5aYTiJOcKRQZUombCA5Wav9RcS1goNh6lRo2dJ0EnEVjaiKeeHh8OefULOm6STiBJ9XH66SKiIup5LqHTSiKtZx+DC0bg2rV5tOIrmU5htImSIJ7Dugy8CIiOucO91/442mk4iraURVrKNIEZg9Gxo3Np1Ecmly/TdVUkXEpUJC4JdfVFK9hYqqWEtUlGN/kTZtTCeRXBhx5kHTEUTEg0VGOvZJbdXKdBLJLyqqYj3n/ly+4w7TSSQH1la4jbmro0zHEBEPFR0Ns2Zpdb+3UVEVawoIgO+/hwc1QucuRsS8YTqCiHioEiVg7lyoXdt0EslvWkwl1vf00/DBB6ZTyBWciIylROoOEhO12l9EnKt8eZg+HUqXNp1ETNCIqljf++/DGxqts7Ivaw1XSRURp6teHf75RyXVm2lEVdzHqFHw+OOgb1lLybD5Ur7EGXbu9TcdRUQ8SIMG8McfULCg6SRikkZUxX306QNffw1+2v7ISn6rN0glVUScqkULmDFDJVVUVMXd3HsvTJoEQUGmk8hZIzMeMx1BRDzIPfc4LlYYFmY6iViBiqq4n06dHOeDwsNNJ/F6m8u05e8VhUzHEBEP8fzz8M03jo1fREBFVdzVDTfAzJlQtKjpJF5tZMl3sNu1iEpE8sbXFz77DN56C2z6lXJFNpuNyZMnm46Rb1RUxX3VqwdLl0KtWqaTeKX4sGv4amVN0zFExM2FhcGvv0KvXqaTZNWzZ09sNhvvvPNOltsnT56MLQdtOjY2lmHDhjk5nfdQURX3VrIkzJsHt91mOonX+ar2MOLjNfQhIrl3zTWOjfzbtTOd5NKCgoIYMmQIJ06ccOnjpKenk5GR4dLHcFcqquL+QkPhp5/gpZdMJ/EadmyM+vcW0zFExI1VqwaLFln7alOtWrUiJiaGt99++7L3mTdvHk2bNiU4OJiSJUvSr18/Tp8+DcANN9zA7t27GTBgADabLXMkdty4cURFRfHLL79QtWpVAgMD2bNnD0uXLuWmm26icOHCREZG0rx5c1asWJEvz9WqVFTFM9hs8Prr8N132hEgH0yv8yybdgSajiEibqplS5g/H0qVMp3kynx9fXnrrbcYMWIE+/btu+j927dvp23bttx+++2sWbOGH374gXnz5tG3b18Afv75Z0qUKMFrr73GgQMHOHDgQObHJiYmMmTIEL744gvWr19PkSJFiI+Pp0ePHsybN49FixZRoUIFbr75ZuLj4/PtOVuNNqQUz9K1K5QrB7fcAuf9QhDnGuHb33QEEXFT/fo5rortLltid+7cmVq1avHKK68wZsyYLO97++236datG/379wegQoUKDB8+nObNm/PJJ59QsGBBfH19CQ8PJyYmJsvHpqam8vHHH3Pttddm3tayZcss9/n888+Jiopizpw5dOjQwTVP0OI0oiqep359xyKrevVMJ/FIO0s05bfl2m1BRHImMBDGjoWPPnKfknrOkCFD+Oqrr9i4cWOW21evXs24ceMICwvLfGnTpg0ZGRns3LnziscMCAigZs2sC1IPHTrEww8/TIUKFYiMjCQiIoKEhAT27Nnj9OfkLtzsW0Ukm4oXd8zQ79kTfvzRdBqP8nG5D8jYp0VUIpJ911wD//sfNGxoOknuNGvWjDZt2vD888/Ts2fPzNsTEhLo3bs3/fr1u+hjSl1lXkNwcPBFuwf06NGDY8eO8dFHH1G6dGkCAwNp1KgRKSkpTnke7khFVTxXcDD88INjxv7gwWC3m07k9s4EFWDM6rqmY4iIG7nuOvj5Z0dZdWfvvPMOtWrVolKlSpm31alThw0bNlC+fPnLflxAQADp6enZeoz58+fz8ccfc/PNNwOwd+9ejh49mrfgbk6n/sXzDRrkGFUNCTGdxO1NqDeUEyf1a0NEsueBB2D2bPcvqQA1atSgW7duDB8+PPO2gQMHsmDBAvr27cuqVavYunUrU6ZMyVxMBY59VOfOncu///571dJZoUIFxo8fz8aNG1m8eDHdunUjODjYZc/JHeh/HPEOd9zh2G+1RAnTSdzaiMNdTEcQETfg5wfDh8OYMY65qZ7itddey7Lfac2aNZkzZw5btmyhadOm1K5dm0GDBlGsWLEsH7Nr1y7KlStHdHT0FY8/ZswYTpw4QZ06dbjvvvvo168fRYoUcdnzcQc2u13nQ8WLHDkC3bvDn3+aTuJ25tbsS/M1I0zHEBGLi4mB77+H5s1NJxFPoBFV8S7R0fD77/Duu+DvbzqNWxkZ8qzpCCJica1awapVKqniPBpRFe+1eDHcfTdcZQsRgX9j6hJ7dClpaVrtLyIX8/GBV15xXCDQR0Ng4kT6dhLv1bAhrFzpmL8qV/RppaEqqSJySTExMH26Y92qSqo4m0ZURQA+/RQGDICkJNNJLCc5IJxSESc5fFT/A4lIVq1awTffQFFdA0RcRP/ziAA88ggsWQJVqphOYjk/1ntXJVVEsvDxgVdfhWnTVFLFtTSiKnK+xETo29dxnT8BoGG1eJasDzMdQ0Qsolgxxyhqixamk4g30DCJyPlCQuDLL2HCBAgPN53GuCVVe6qkikimrl1h3TqVVMk/Kqoil3LPPbBiBdSpYzqJUSOiXjYdQUQsoEAB+PZb+O47x79F8otO/YtcSUoKDBwIH30EXvajcrhwVUqeWkdKilb7i3izm25yzIYqXtx0EvFGGlEVuZKAABg6FObMgYoVTafJV59X+0glVcSLBQfDiBGOBVMqqWKKRlRFsispCQYPhvffh/R002lcKs03kNjo0/x70Nd0FBExoF49x4KpSpVMJxFvpxFVkewKCoJ33nFc0eraa02ncan/1X9LJVXEC/n7O64wtXChSqpYg4qqSE7VrQvLlsEbb0BgoOk0LjEi8UHTEUQkn113nWMN6eDB4OdnOo2Ig4qqSG74+cGLLzouwdqokek0TrW64p38sybSdAwRySdhYY71ovPnQ/XqptOIZKWiKpIXVarAvHkwbBiEhppO4xQji75mOoKI5JObb4b166FfP8fVpkSsRoupRJxl507o1QumTzedJNdORMZSPGUHZ85otb+IJytSxPH39d13m04icmX6+0nEWcqUgb//hjFjICrKdJpcGVNrhEqqiIfr0QM2blRJFfegEVURVzhwAB5/HCZNMp0k2zJsvpQvcYade/1NRxERF6haFYYPhxtvNJ1EJPs0oiriCtdcAz/9BLNmQa1aptNky9R6g1VSRTxQZKTjuiWrV6ukivtRURVxpRtugOXLYfRoKFrUdJorGpn+qOkIIuJENhs88ABs2QL9+2vLKXFPOvUvkl9OnYI333TsA5OcbDpNFpvKtKPqrt+w2zU/VcQTNGgAI0dC/fqmk4jkjUZURfJLRAQMGQIbNsBtt5lOk8XIEu+opIp4gKJF4csvYdEilVTxDBpRFTFlzhwYMMBx0QCD4sOuobjtX+LjVVRF3FVgIPTtC4MGOf4mFvEUGlEVMaV5c8elWL/4AmJijMUYV2e4SqqIm/Lxgfvug82b4f33VVLF82hEVcQK4uPhrbccS3Pzcf6qHRtVypxh887AfHtMEXGOdu3gnXegZk3TSURcRyOqIlYQHg5vvw2bNkGXLo7luvng7zoDVVJF3EyDBo6d737/XSVVPJ+KqoiVxMbCDz845q3edpvLC+sI3ydcenwRcZ6KFWHiRFi82LHznYg30Kl/EStbuxZef91x8QAn/6juLNGU8vvnkJGh+akiVlayJLzwAjz0kPZCFe+jEVURK6tRA3780VFY77rLsXLCSUaV+1AlVcTCSpeGTz+FbdvgkUdUUsU7aURVxJ1s3AhvvAHffw8ZGbk+TGJwIUoEHubESf2tKmI15crB889D9+7gr6sai5fT/1Ii7qRKFZgwwXHRgHvvBV/fXB1mQr0PVVJFLKZiRRg3zrGm8sEHVVJFQCOqIu5t61bHCOuECZCenu0Pq1khkbVbg10YTESyq0oVeOklx+yeXP7tKeKxNKQi4s4qVICvvnLs9n3//dmaxDan5uMqqSIW0KQJTJoE69bBPfeopIpcikZURTzJrl0wYgSMGQNxcZe8y53X7eWnRSXyN5eIAI4yevvt8NRTjv1QReTKVFRFPFFCgmOy2/DhjukBZ+2LqUeZo0tIS9Nqf5H8FBHh2F6qXz/Han4RyR6d+hfxRGFh0LevY0rA1KnQqhUAn1QappIqko9KlYIPPoC9ex2vVVJFckYjqiLeYv16XhxTho8+D+H0adNhRDxb8+bw6KNwxx2aeyqSFyqqIl4mLs6x/urjjx0DriLiHAULQo8e0KsXVK5sOo2IZ1BRFfFiM2bA55/DlCmQnGw6jYh7uv566N3bMXoaFGQ6jYhnUVEVEY4fh2+/hS+/hJUrTacRsb6oKMeVo3r1gmrVTKcR8VwqqiKSxerVMHas4xoCR4+aTiNiHT4+0LIl3Hcf3HknBGs7YhGXU1EVkUtKSYFff3WMsk6blqMLX4l4lGuvdZTTu++GYsVMpxHxLiqqInJV+/fDjz/CDz/AokWm04i4XsmSjqtF3XsvVK9uOo2I91JRFZEc2b3bUVp//BGWLTOdRsR5IiMdC6LuvdexvZRNWw6LGKeiKiK5tn37f6V11SrTaURyrkgRuOUWuO02x/zTgADTiUTkfCqqIuIUW7Y4pgZMnuzYOUC/WcSqYmOhc2fHS5MmjkVSImJNKqoi4nT798Nvvzmu3jp9OiQmmk4k3q5qVceoaefOUKeO6TQikl0qqiLiUklJMGuWo7ROnQp79phOJN4gNBRatIA2baBtWyhf3nQiEckNFVURyVdr1zoK619/wcKFuiKWOIfN5thGqk0bx0uTJppvKuIJVFRFxJikJJg/H2bOdLwsWwZpaaZTibsoUgRuuslRTFu3hqJFTScSEWdTURURy4iPh3/++a+4rl4NGRmmU4lVxMZC06b/vVSubDqRiLiaiqqIWNbx444R10WLHC9LlzrKrHg+m82x0f65Unr99VCihOlUIpLfVFRFxG1kZMCGDf8V18WLHW9r1NX9FS4M9eo5Xho2dMwxLVDAdCoRMU1FVUTcWnw8LFniGG1dvRrWrHHs6aq5rtZVpAjUru14qVvXUU5jY02nEhErUlEVEY+TnAwbNzpK67mXtWvh4EHTybxLaKhjHmmVKo6XmjUd5bR4cdPJRMRdqKiKiNc4csRRWDdvhm3b/nvZvl3bZOVFdPR/ZfT8YlqypGOuqYhIbqmoiojXy8iAffuyltdt22DXLsdVtg4f9u5LwhYsCKVLX/olNhYKFTKdUEQ8lYqqiMhVpKY6pg3s3w///pv19f79cOCAY4eCEycce8O6Az8/xwKmIkUcI6IXvi5e/L8yGh5uOq2IeCsVVRERJ0pKchTWkycdry98OX3aMc0gKcnx+tzLhW+npDhOm/v4gK+v4/WF/z73dmAghIX99xIaeul/h4c7Smh0tGNFvU7Li4jVqaiKiIiIiCX5mA4gIiIiInIpKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkkqqiIiIiJiSSqqIiIiImJJKqoiIiIiYkn/B5QUN8DTv6tJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt # type: ignore\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Muat data uji\n",
    "# X_test = pd.read_csv('HASIL-RISET/X_test.csv')\n",
    "\n",
    "# # Muat model dari file joblib\n",
    "# nb_model = joblib.load('HASIL-RISET/svm_model.pkl')\n",
    "\n",
    "# Lakukan prediksi pada data uji\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "# Ekspor hasil prediksi ke file CSV\n",
    "predictions_df = pd.DataFrame(y_pred, columns=['predicted_label'])\n",
    "predictions_df.to_csv('HASIL-RISET/predict_SVM.csv', index=False)\n",
    "\n",
    "# Hitung jumlah prediksi untuk setiap kelas\n",
    "unique, counts = np.unique(y_pred, return_counts=True)\n",
    "sentiment_counts = dict(zip(unique, counts))\n",
    "\n",
    "# Buat pie chart\n",
    "labels = sentiment_counts.keys()\n",
    "sizes = sentiment_counts.values()\n",
    "colors = ['red', 'blue', 'green']\n",
    "# explode = (0.1, 0, 0)  # Hanya meledakkan bagian pertama (Negatif)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%',\n",
    "        shadow=False, startangle=140)\n",
    "plt.title('Distribusi Sentimen pada Data Uji')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Mendapatkan classification report dalam bentuk dictionary\n",
    "report = classification_report(y_test, y_pred, output_dict=True)\n",
    "\n",
    "# Mengonversi ke DataFrame untuk visualisasi\n",
    "df_report = pd.DataFrame(report).T\n",
    "\n",
    "# Menghapus support karena bukan metrik evaluasi\n",
    "df_report = df_report.drop(columns=['support'])\n",
    "\n",
    "# Membuat heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(df_report, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "plt.title(\"Classification Report Heatmap\")\n",
    "plt.ylabel(\"Classes & Averages\")\n",
    "plt.xlabel(\"Metrics\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1 entries, 0 to 0\n",
      "Data columns (total 5 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Confusion Matrix         1 non-null      object \n",
      " 1   Classification Report    1 non-null      object \n",
      " 2   Cross-Validation Scores  1 non-null      object \n",
      " 3   Mean CV Score            1 non-null      float64\n",
      " 4   Std Dev CV Score         1 non-null      float64\n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 172.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('HASIL-RISET/evaluation_results_SVM.csv')\n",
    "df.info()"
   ]
  }
 ],
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