OPTIMASI KLASIFIKASI PENYAKIT GINJAL KRONIS (PGK) MENGGUNAKAN KOMBINASI METODE SELEKSI FITUR DAN ALGORITMA MACHINE LEARNING
Abstract
enyakit ginjal kronis (PGK) merupakan salah satu penyakit yang memiliki tingkat prevalensi tinggi dan sering terlambat terdeteksi karena minimnya gejala pada tahap awal. Oleh karena itu, diperlukan metode klasifikasi yang akurat untuk membantu proses deteksi dini. Penelitian ini bertujuan untuk mengoptimalkan kinerja model klasifikasi PGK dengan mengkombinasikan metode seleksi fitur dan model algoritma Machine Learning. Metode seleksi fitur yang digunakan dalam penelitian ini meliputi Information Gain (IG), Gain Rasio (GR), dan Fast Correlation Based Filter (FCBF). Sedangkan algoritma klasifikasi yang digunakan adalah Naive Bayes, Support Vector Machine (SVM), Tree, Random Forest, dan K-Nearest Neighbor (KNN). Dataset yang digunakan adalah Chronic Kidney Disease Dataset yang diperoleh dari UCI Machine Learning Repository. Proses evaluasi dilakukan menggunakan metode k-fold cross validation dengan metrik akurasi. Hasil penelitian menunjukkan bahwa penggunaan seleksi fitur mampu meningkatkan performa model klasifikasi 5.84%. Kombinasi terbaik diperoleh pada metode Gain Ratio dengan algoritma Naive Bayes yang menghasilkan akurasi sebesar 99.9%. Selain itu, seleksi fitur juga berhasil mengurangi jumlah fitur yang digunakan sehingga meningkatkan efisiensi model. Penelitian ini membuktikan bahwa optimasi melalui kombinasi metode seleksi fitur dan algoritma Machine Learning dapat meningkatkan akurasi dan efisiensi dalam klasifikasi penyakit ginjal kronis, serta berpotensi untuk diterapkan dalam sistem pendukung keputusan di bidang medis
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