{
      "abstract": "Diabetes Mellitus (DM) merupakan salah satu penyakit kronis dengan\r\ntingkat prevalensi yang terus meningkat di dunia, termasuk di Indonesia. Penyakit\r\nini terbagi menjadi dua tipe utama, yaitu DM tipe 1 dan tipe 2, yang masing-masing\r\nmembutuhkan penanganan yang berbeda. Oleh karena itu, klasifikasi yang akurat\r\nantara kedua tipe DM sangat penting untuk mendukung diagnosis dini dan\r\nintervensi medis yang tepat.\r\nPenelitian ini bertujuan untuk membangun model klasifikasi penyakit\r\nDiabetes Mellitus menggunakan algoritma Support Vector Machine (SVM).\r\nDataset penelitian terdiri dari sejumlah atribut klinis pasien yang telah melalui tahap\r\npreprocessing, termasuk penanganan missing values, penghilangan outlier\r\nmenggunakan Interquartile Range (IQR), normalisasi dengan StandardScaler, dan\r\nkonversi label menggunakan Label Encoding. Karena jumlah data antar kelas tidak\r\nseimbang, diterapkan metode Synthetic Minority Over-sampling Technique\r\n(SMOTE) untuk mengatasi masalah ketidakseimbangan kelas.\r\nEvaluasi model dilakukan menggunakan metrik akurasi, precision, recall,\r\nF1-score, dan confusion matrix. Hasil evaluasi menunjukkan bahwa model SVM\r\ndengan kernel linier memberikan akurasi cukup, yaitu sebesar 73%, dengan nilai\r\nF1-score seimbang untuk kedua kelas. Hal ini menunjukkan bahwa model mampu\r\nmelakukan klasifikasi dengan performa yang baik dan tanpa bias terhadap salah\r\nsatu kelas.",
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      "title": "KLASIFIKASI PENYAKIT DIABETES MELLITUS\r\nMENGGUNAKAN ALGORITMA SVM",
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