Penerapan XGBoost dan SMOTE untuk Klasifikasi Metode Pembayaran Pelanggan pada Data Transaksi Tidak Seimbang

Abstract

The increasing use of digital payment methods in retail transactions highlights the importance of analyzing customer payment behavior. This study aims to classify customer payment methods using the XGBoost algorithm and to evaluate the effect of Synthetic Minority Over-sampling Technique (SMOTE) in handling class imbalance. The dataset consists of 287,422 transaction records processed using the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which includes data understanding, data preparation, modeling, and evaluation stages. Experimental results show that the XGBoost model without SMOTE achieved an accuracy of 92.83% and a ROC-AUC of 0.7759, but performed poorly in identifying the minority class (Card), with a recall of 0.14, indicating a strong bias toward the majority class. After applying SMOTE, the model’s ability to detect the minority class improved, with recall increasing to 0.53 and F1-score reaching 0.28, although accuracy decreased to 78.50% and ROC-AUC to 0.7529. This study contributes by implementing XGBoost combined with the SMOTE method for customer payment method classification on imbalanced data and evaluating model performance using multiple classification metrics. This trade-off indicates that SMOTE improves sensitivity toward minority classes while affecting overall predictive accuracy. The findings highlight that evaluation of imbalanced classification models should not rely solely on accuracy but must also consider precision, recall, F1-score, and ROC-AUC to obtain a more comprehensive assessment. Overall, while SMOTE enhances minority class detection, further improvements are still required to achieve more stable and reliable classification performance.