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All Journal Repeater: Publikasi Teknik Informatika dan Jaringan
Yilmaz Trigumari Syah Putra
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Computer Science & IT

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Implementing XGBoost Model for Predicting Customer Churn in E-Commerce Platforms Andy Hermawan; Aji Saputra; Muhammad Dhika Rafi; Syafiq Basmallah; Yilmaz Trigumari Syah Putra; Wafa Nabila
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 2 (2025): April: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i2.401

Abstract

Customer churn is a major challenge in e-commerce, directly affecting revenue and profit. This study aims to develop a machine learning model using XGBoost to predict churn probability. To handle class imbalance, SMOTE was applied as a resampling method, and hyperparameter tuning was performed to enhance performance. The model was evaluated using the F2-score, prioritizing recall while maintaining precision. The results show that the XGBoost model with SMOTE achieves strong performance, with an F2-score of 0.849 on the tuned test data. This model can help businesses identify at-risk customers early, enabling proactive retention strategies.
Co-Authors Aji Saputra Andy Hermawan Muhammad Dhika Rafi Syafiq Basmallah Wafa Nabila