CHURN ANALISIS PADA DATA PELANGGAN TELEKOMUNIKASI MENGGUNAKAN ENSEMBLE LEARNING

Authors

  • Muthia Nadhira Faladiba universitas islam bandung
  • Rizqi Haryastuti PT Visionet Internasional (OVO)

DOI:

https://doi.org/10.32493/sm.v5i1.31934

Abstract

Intense competition in broadband services will create high opportunities for consumers to switch providers, such as conditions that arise in competition for SMS, telephone, and internet services. The churn rate is the percentage of consumers who stop subscribing to the service. Ideally, this churn percentage is only 5% – 10%, and if it exceeds this figure, it indicates the company's inability to retain customers. A high churn rate indicates a decline in the cellular operator's market share and affects the company's revenue. Based on these problems, it is necessary to analyze the churn behavior of broadband subscribers to determine the dissatisfaction factors of cellular telecommunications consumers. Then predictions are made for customers who tend to churn from provider companies and determine the characteristics of churn and stay customers. The ensemble method is used to detect churn, which consists of several methods, including random forest, boosting, and super learner. Random Forest is proven to produce the best classification method with an excellent ability to predict customer churn, which is 80.1%, with an average usage time of 3 years.

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Classification. Proceedings of 2020 the 10th International Workshop on Computer

Science and Engineering. 2020 the 10th International Workshop on Computer Science

and Engineering. https://doi.org/10.18178/wcse.2020.02.024

Rajeswari, P. S., & Ravilochanan, P. (2014). Churn Analytics on Indian Prepaid Mobile

Services. Asian Social Science, 10(13), p169. https://doi.org/10.5539/ass.v10n13p169

Rizal, Y. (2017). Evaluasi Strategi Pengembangan Jaringan Telekomunikasi dengan Blue

Ocean Strategy. Jurnal Telekomunikasi dan Komputer, 6(1), 45.

https://doi.org/10.22441/incomtech.v6i1.1148

Sun, Y., Wong, A. K. C., & Kamel, M. S. (2009). CLASSIFICATION OF IMBALANCED

DATA: A REVIEW. International Journal of Pattern Recognition and Artificial

Intelligence, 23(04), 687–719. https://doi.org/10.1142/S0218001409007326

Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., & Kim, S. W. (2019). A Churn

Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for

Churn Prediction and Factor Identification in Telecom Sector. IEEE Access, 7, 60134–

https://doi.org/10.1109/ACCESS.2019.2914999

Y., N. N., Ly, T. V., & Son, D. V. T. (2022). Churn prediction in telecommunication industry

using kernel Support Vector Machines. PLOS ONE, 17(5), e0267935.

https://doi.org/10.1371/journal.pone.0267935

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Published

2023-01-31

Issue

Vol. 5 No. 1 (2023)

Section

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