Comparative Classification of Hepatitis C Disease Using Naïve Bayes and Random Forest with SMOTE-Based Class Balancing

Ida Oktavia Salsavana; Mokhamad Amin Hariyadi; Fresy Nugroho

doi:10.55681/armada.v4i6.2961

Authors

Ida Oktavia Salsavana Program Studi Magister Informatika, Universitas Islam Negri Maulana Malik IbrahimMalang
Mokhamad Amin Hariyadi Program Studi Magister Informatika, Universitas Islam Negri Maulana Malik Ibrahim Malang
Fresy Nugroho Program Studi Magister Informatika, Universitas Islam Negri Maulana Malik Ibrahim Malang

DOI:

https://doi.org/10.55681/armada.v4i6.2961

Keywords:

Hepatitis C, Ketidakseimbangan Kelas, Naïve Bayes, Random Forest, SMOTE

Abstract

Hepatitis C merupakan penyakit hati yang disebabkan oleh Virus Hepatitis C (HCV) dan dapat berkembang menjadi komplikasi serius seperti sirosis, gagal hati, dan kanker hati. Deteksi dini yang akurat sangat penting untuk meningkatkan keberhasilan pengobatan dan kualitas hidup pasien. Penelitian ini membandingkan kinerja algoritma Naïve Bayes dan Random Forest dalam klasifikasi penyakit Hepatitis C menggunakan data klinis dari UCI Machine Learning Repository yang terdiri atas 615 data pasien. Tahap prapemrosesan meliputi pembersihan data, transformasi data, dan penyeimbangan kelas menggunakan metode Synthetic Minority Over-sampling Technique (SMOTE). Dataset dibagi dengan rasio 80:20 untuk data latih dan data uji. Hasil eksperimen menunjukkan bahwa Random Forest menghasilkan akurasi 99,06%, precision 0,99, recall 0,98, dan F1-score 0,99, jauh melampaui Naïve Bayes yang memperoleh akurasi 84,06%. Analisis feature importance mengidentifikasi AST, ALT, GGT, Usia, dan Albumin sebagai prediktor klinis paling signifikan. Kombinasi algoritma Random Forest dan SMOTE terbukti menjadi pendekatan yang sangat efektif dalam klasifikasi Hepatitis C dan berpotensi besar mendukung pengambilan keputusan klinis yang akurat untuk deteksi dini penyakit.

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