📅 12 March 2025
DOI: 10.62951/repeater.v3i2.401
Implementing XGBoost Model for Predicting Customer Churn in E-Commerce Platforms
Repeater : Publikasi Teknik Informatika dan Jaringan
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 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.
🔖 Keywords
#Churn Prediction; E-commerce; Machine Learning; XGBoost
ℹ️ Informasi Publikasi
Tanggal Publikasi
12 March 2025
Volume / Nomor / Tahun
Volume 3, Nomor 2, Tahun 2025
📝 HOW TO CITE
Andy Hermawan; Aji Saputra; Muhammad Dhika Rafi; Syafiq Basmallah; Yilmaz Trigumari Syah Putra; Wafa Nabila, "Implementing XGBoost Model for Predicting Customer Churn in E-Commerce Platforms," Repeater : Publikasi Teknik Informatika dan Jaringan, vol. 3, no. 2, Mar. 2025.
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