Prediksi Churn Pelanggan Telekomunikasi Menggunakan Segmentasi Pelanggan dan Explainable Artificial Intelligence Berbasis SHAP
DOI:
https://doi.org/10.55606/jtmei.v5i1.6088Keywords:
Customer Churn, Customer Segmentation, K-Means, Machine Learning, SHapley Additive exPlanationsAbstract
The telecommunications industry faces intense competition that makes customer churn a critical threat to business sustainability. This study proposes an integrated analytical framework that combines churn prediction, customer segmentation, and model interpretability to support data-driven retention strategies. Three machine learning algorithms Logistic Regression, Random Forest, and XGBoost were trained on the IBM Telco dataset (n = 7,043) with class imbalance handled through SMOTE. K-Means clustering was then applied to predicted-churn customers, and SHAP (SHapley Additive exPlanations) was used to interpret the best-performing model both globally and at the segment level. Random Forest achieved the best performance (Accuracy 0.7763, F1-Score 0.6114, ROC-AUC 0.833), and three customer segments were identified: New High-Risk, Loyal High-Value, and Low-Tier Economic. The per-segment SHAP analysis revealed heterogeneous churn drivers across segments, enabling the formulation of more targeted and actionable retention strategies. The integration of churn prediction, segmentation, and SHAP therefore provides a more precise foundation for data-driven retention decision-making in the telecommunications industry.
Downloads
References
Adeniran, I. A., Efunniyi, C. P., Osundare, O. S., & Abhulimen, A. O. (2024). Implementing machine learning techniques for customer retention and churn prediction in telecommunications. Computer Science & IT Research Journal, 5(8), 2011–2025. https://doi.org/10.51594/csitrj.v5i8.1489
Bauer, K., von Zahn, M., & Hinz, O. (2023). Expl(AI)ned: The impact of explainable artificial intelligence on users’ information processing. Information Systems Research, 34(4), 1582–1602. https://doi.org/10.1287/isre.2023.1199
Chang, V., Hall, K., Xu, Q., Amao, F., Ganatra, M., & Benson, V. (2024). Prediction of customer churn behavior in the telecommunication industry using machine learning models. Algorithms, 17(6), Article 231. https://doi.org/10.3390/a17060231
Esfahani, H. H., Toyonaga, S., & Oyibo, K. (2025). The application of explainable artificial intelligence in the prediction, diagnoses, treatment, and management of chronic diseases: A systematic review. Digital Health, 11. https://doi.org/10.1177/20552076251355669
Harani, N. H., Prianto, C., & Nugraha, F. A. (2020). Segmentasi pelanggan produk digital service IndiHome menggunakan algoritma K-Means berbasis Python. Jurnal Manajemen Informatika (JAMIKA), 10(2), 133–146. https://doi.org/10.34010/jamika.v10i2.2683
IBM. (2018). Telco customer churn dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/blastchar/telco-customer-churn
Imani, M., Ghaderpour, Z., Joudaki, M., & Beikmohammadi, A. (2024). The impact of SMOTE and ADASYN on random forest and advanced gradient boosting techniques in telecom customer churn prediction. Preprints. https://doi.org/10.20944/preprints202403.0213.v2
Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer churn prediction system: A machine learning approach. Computing, 104(2), 271–294. https://doi.org/10.1007/s00607-021-00908-y
Leung, C. K., Pazdor, A. G. M., & Souza, J. (2021). Explainable artificial intelligence for data science on customer churn. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1–10). IEEE. https://doi.org/10.1109/DSAA53316.2021.9564166
Mahmud, S., Islam, B. U., Anik, N. H., & Ghosh, T. (2024). Diabetes prediction: A comparative analysis of machine learning algorithms with SMOTE. In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS) (pp. 1–6). IEEE. https://doi.org/10.1109/COMPAS60761.2024.10796405
Noviandy, T. R., Idroes, G. M., Hardi, I., Afjal, M., & Ray, S. (2024). A model-agnostic interpretability approach to predicting customer churn in the telecommunications industry. Infolitika Journal of Data Science, 2(1), 34–44. https://doi.org/10.60084/ijds.v2i1.199
Onoja, J. P. (2025). A comprehensive review of customer churn prediction models in telecommunications.
Özkurt, C. (2025). Comparative analysis of XAI techniques on telecom churn prediction using SHAP and interpreted ML partial dependence. Türk Doğa ve Fen Dergisi, 14(2), 11–25. https://doi.org/10.46810/tdfd.1529139
Salehi, A. R., & Khedmati, M. (2024). A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data. Scientific Reports, 14(1), Article 5152. https://doi.org/10.1038/s41598-024-55598-1
Sikri, A., Jameel, R., Idrees, S. M., & Kaur, H. (2024). Enhancing customer retention in telecom industry with machine learning driven churn prediction. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-63750-0
Wu, S., Yau, W.-C., Ong, T.-S., & Chong, S.-C. (2021). Integrated churn prediction and customer segmentation framework for telco business. IEEE Access, 9, 62118–62136. https://doi.org/10.1109/ACCESS.2021.3073776
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Ika Kemala Sawati Azzahra, I Made Artha Agastya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
