EVALUASI MODEL KLASIFIKASI DALAM DETEKSI PENIPUAN TRANSAKSI: STUDI KASUS PADA DATA TIDAK SEIMBANG

Abstract

The rise in digital transactions increases the risk of credit card fraud, highlighting the need for a smart and accurate detection system. This study aims to develop a classification model that can effectively detect fraudulent transactions, despite data imbalance challenges. Data processing involves the use of the SMOTE technique to improve the representation of minority classes and Optuna for hyperparameter tuning. Three machine learning models are applied: Logistic Regression, Random Forest, and XGBoost. Model performance is evaluated using precision, recall, f1-score, and ROC-AUC. The results show that Random Forest achieves the best performance, with a precision of 0.91, recall of 0.74, and f1-score of 0.82. Logistic Regression achieves high recall but very low precision, while XGBoost produces a competitive AUC but a lower f1-score than Random Forest. This research highlights the importance of algorithm selection, data balancing with SMOTE, and parameter tuning to build an effective and adaptive fraud detection system for imbalanced data