Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Multidisiplin Sahombu
Kamil, Yasin
Unknown Affiliation
Arts Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparative Analysis of SMOTE-Based Random Forest and XGBoost Algorithms for Handling Imbalanced Datasets in Credit Card Fraud Detection Azizan, Muhamad Lutfi; Kamil, Yasin; Ismau, Septiano Alvian; Sandi, Dede; Maulana, Ihsan; Nursodiq, Ahmad
Jurnal Multidisiplin Sahombu Vol. 6 No. 01 (2026): Jurnal Multidisiplin Sahombu, January 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of digital payment systems has increased the complexity and risk of credit card fraud, particularly due to the highly imbalanced nature of transaction data. This study aims to compare the performance of Random Forest and XGBoost algorithms combined with the Synthetic Minority Over sampling Technique in detecting fraudulent credit card transactions. The proposed approach focuses on improving classification effectiveness by addressing class imbalance and reducing bias toward legitimate transactions. Data preprocessing includes normalization, stratified data splitting, and the application of over sampling techniques on the training dataset. Model performance is evaluated using precision, recall, F score, and the area under the receiver operating characteristic curve, which are more appropriate for imbalanced classification problems. The findings indicate that Random Forest demonstrates more stable and balanced performance, particularly in minimizing false fraud alerts while maintaining adequate fraud detection capability. These results suggest that Random Forest with over sampling provides a practical and reliable solution for real world credit card fraud detection systems.
Co-Authors Ahmad Nursodiq Azizan, Muhamad Lutfi Ismau, Septiano Alvian Maulana, Ihsan Sandi, Dede