Churn Prediction Analysis Of Customer Ferry Operator "Batamfast" Using Machine Learn-Ing With Supervised Classification Model
DOI:
https://doi.org/10.59188/eduvest.v5i1.1669Keywords:
Churn Prediction, Batamfast, machine learning, feature importance, Random Forest, XGBoost, Gradient BoostingAbstract
BatamFast is the first ferry operator in Batam and has been serving international routes to Singapore and Malaysia since 1985. Until 2010, BatamFast was the only ferry operator in Batam. However, since 2011, competitors such as Sindo Ferry, Horizon Ferry, and Majestic have emerged, increasing competition to four ferry operators in Batam by 2024. This study aims to measure the churn rate of BatamFast customers and identify the factors causing it using machine learning prediction models such as Random Forest, XGBoost, and Gradient Boosting. In addition to churn prediction, feature importance analysis was conducted to determine the significant features influencing customer decisions. The results indicate that XGBoost is the best model compared to Random Forest and Gradient Boosting. Key factors for churn are customer category, payment method, and booking mode. These findings are expected to help BatamFast reduce churn, improve customer satisfaction, and strengthen its competitive position in the international ferry market.
References
Agarwal, V., Taware, S., Yadav, S. A., Gangodkar, D., Rao, A. L. N., & Srivastav, V. K. (2022). Customer-Churn Prediction Using Machine Learning. 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 893–899.
Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1), 1–24.
Bhuse, P., Gandhi, A., Meswani, P., Muni, R., & Katre, N. (2020). Machine learning based telecom-customer churn prediction. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 1297–1301.
De Caigny, A., Coussement, K., De Bock, K. W., & Lessmann, S. (2020). Incorporating textual information in customer churn prediction models based on a convolutional neural network. International Journal of Forecasting, 36(4), 1563–1578.
Dhangar, K., & Anand, P. (2021). A Review on Customer Churn Prediction Using Machine Learning Approach. International Journal of Innovations in Engineering Research and Technology, 8(05), 193–201.
Jain, H., Khunteta, A., & Srivastava, S. (2020). Churn prediction in telecommunication using logistic regression and logit boost. Procedia Computer Science, 167, 101–112.
Kim, S., & Lee, H. (2022). Customer churn prediction in influencer commerce: An application of decision trees. Procedia Computer Science, 199, 1332–1339.
Liu, K., Hu, X., Zhou, H., Tong, L., Widanage, W. D., & Marco, J. (2021). Feature analyses and modeling of lithium-ion battery manufacturing based on random forest classification. IEEE/ASME Transactions on Mechatronics, 26(6), 2944–2955.
Loria, E., & Marconi, A. (2021). Exploiting limited players’ behavioral data to predict churn in gamification. Electronic Commerce Research and Applications, 47, 101057.
Mohammad, N. I., Ismail, S. A., Kama, M. N., Yusop, O. M., & Azmi, A. (2019). Customer churn prediction in telecommunication industry using machine learning classifiers. Proceedings of the 3rd International Conference on Vision, Image and Signal Processing, 1–7.
Nurhidayat, M. M. S., & Anggraini, D. (2023). Analysis and Classification of Customer Churn Using Machine Learning Models. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(6), 1253–1259.
Osman, Y., & Ghaffari, B. (2021). Customer churn prediction using machine learning: A study in the B2B subscription based service context.
Prabadevi, B., Shalini, R., & Kavitha, B. R. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, 4, 145–154.
Raeisi, S., & Sajedi, H. (2020). E-commerce customer churn prediction by gradient boosted trees. 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), 55–59.
Shrestha, S. M., & Shakya, A. (2022). A customer churn prediction model using XGBoost for the telecommunication industry in Nepal. Procedia Computer Science, 215, 652–661.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ryan Tri Pamungkas, Nenden Siti Fatonah, Gerry Firmansyah, Budi Tjahjono
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
