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All Journal Jurnal Algoritma
Rayhan Irawan
Universitas Jenderal Achmad Yani
Computer Science & IT

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Perbandingan Fungsi Kernel Pada Algoritma SVM untuk klasifikasi Kredit Macet Rayhan Irawan; Yulison Herry Chrisnanto; Gunawan Abdilah
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.2938

Abstract

Non-performing loans are a significant problem for financial institutions as they can disrupt economic stability and cause financial losses. To address this issue, this study applies the Support Vector Machine (SVM) algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE) to improve the accuracy of classifying customers at risk of loan default. The study compares three kernel functions: linear, polynomial, and RBF. The experimental results show that the RBF kernel achieves the best performance with an accuracy of 0.76 (76%), followed by the polynomial kernel at 0.73 (73%) and the linear kernel at 0.72 (72%). This approach proves effective in improving credit risk prediction accuracy through data distribution balancing using SMOTE.
Co-Authors Gunawan Abdilah Yulison Herry Chrisnanto