Implementasi Algoritma Decision Tree untuk Klasifikasi Penyakit Ginjal Kronis Berbasis Machine Learning
Implementation of Decision Tree Algorithm for Chronic Kidney Disease Classification Based on Machine Learning
Keywords:
Decision Tree, machine learning, Penyakit Ginjal Kronis, Klasifikasi, Deteksi diniAbstract
Penyakit Ginjal Kronis (PGK) merupakan penyakit tidak menular yang membutuhkan deteksi dini secara cepat, akurat, dan mudah diinterpretasikan. Penelitian ini bertujuan menerapkan algoritma Decision Tree berbasis machine learning untuk mengklasifikasikan PGK berdasarkan data medis pasien, mengidentifikasi fitur yang paling berpengaruh, serta mengevaluasi kinerja model melalui metrik akurasi, precision, recall, F1-score, dan confusion matrix. Data yang digunakan merupakan dataset sekunder Kidney_Disease.csv yang memuat atribut demografis, pemeriksaan klinis, pemeriksaan laboratorium, riwayat penyakit, dan kelas target CKD atau Non-CKD. Tahapan penelitian meliputi data overview, exploratory data analysis, pembersihan data, imputasi nilai hilang, label encoding, pembagian data latih dan data uji dengan proporsi 70:30, pembangunan model Decision Tree Classifier, dan evaluasi model. Hasil penelitian menunjukkan akurasi data latih sebesar 96,79% dan akurasi data uji sebesar 94,17%. Model menghasilkan nilai precision, recall, dan F1-score yang tinggi pada kedua kelas. Fitur haemoglobin, serum creatinine, specific gravity, blood glucose random, dan packed cell volume menjadi variabel penting dalam proses klasifikasi. Hasil ini menunjukkan bahwa Decision Tree dapat menjadi alat bantu skrining awal PGK yang akurat, transparan, dan mudah dipahami.
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