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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Yogi Prasetyo Hernoto
Institut Teknologi dan Bisnis STIKOM Bali
Computer Science & IT Engineering

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Enhancing E-Commerce Churn Prediction Accuracy Through the Combination of Gradient Boosting and Feature Selection Yogi Prasetyo Hernoto; Dandy Pramana Hostiadi; Putu Desiana Wulaning Ayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7076

Abstract

As the number of e-commerce users continues to expand, analyzing user activity has become essential for improving service quality, since customers are a key factor in strengthening competitiveness and business performance in this industry. Churn, in which customers discontinue service due to dissatisfaction, requires accurate detection to support effective retention strategies. To improve churn prediction accuracy, this study explores the optimal combination of feature selection methods and machine learning classifiers. ANOVA F-Test, Information Gain, Chi-Square, Recursive Feature Elimination (RFE), Variance Threshold, and Least Absolute Shrinkage and Selection Operator (LASSO) are the six feature selection techniques that are assessed in this study. These are combined with three classification algorithms: Gradient Boosting (GB), Random Forest (RF), and Logistic Regression (LR). Gradient Boosting with either Information Gain or LASSO yields the best results, achieving approximately 98% accuracy, 97% precision, 92% recall, 96% F1-score, and 99% ROC-AUC, based on testing on a publicly available Kaggle dataset. This approach outperforms prior studies, providing a robust framework for e-commerce developers to anticipate customer loss. By identifying key predictors of churn, the proposed model offers actionable insights to enhance service and enable proactive customer management.
Co-Authors Dandy Pramana Hostiadi Putu Desiana Wulaning Ayu