Analisis Klasifikasi Hepatitis Menggunakan Synthetic Minority Oversampling Technique, Support Vector Machine, dan Random Forest

Amalia Nur Laily; Mula Agung Barata; Denny Nurdiansyah

doi:10.51454/decode.v6i1.1630

Authors

Amalia Nur Laily Teknik Informatika Universitas Nahdlatul Ulama Sunan Giri
Mula Agung Barata Teknik Informatika Universitas Nahdlatul Ulama Sunan Giri
Denny Nurdiansyah Statistika Universitas Nahdlatul Ulama Sunan Giri

DOI:

https://doi.org/10.51454/decode.v6i1.1630

Keywords:

Hepatitis, Random Forest, SMOTE, Support Vector Machine

Abstract

Hepatitis akibat infeksi virus masih menjadi masalah kesehatan masyarakat yang serius sehingga deteksi dini berbasis data klinis penting untuk mencegah kerusakan hati lebih lanjut. Penelitian ini menganalisis kinerja algoritma Support Vector Machine (SVM) dan Random Forest pada klasifikasi hepatitis serta mengkaji dampak penerapan Synthetic Minority Over-sampling Technique (SMOTE). Dataset yang digunakan adalah HepatitisCdata.csv dari Kaggle dengan 615 data pasien yang memuat atribut demografis dan parameter biokimia hati. Tahapan penelitian meliputi preprocessing data, penanganan outlier, transformasi atribut kategorikal, serta pembangunan model baseline dan SMOTE. Evaluasi dilakukan menggunakan 10-fold cross-validation dengan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa SMOTE meningkatkan performa kedua algoritma, dengan Random Forest + SMOTE memberikan hasil terbaik (akurasi 98,85%) dibandingkan SVM + SMOTE (98,50%). Kontribusi penelitian ini terletak pada penggunaan pipeline preprocessing dan evaluasi yang seragam untuk membandingkan dampak SMOTE secara langsung pada dua algoritma klasifikasi hepatitis.

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