International Journal of


EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 8, Issue 12 (December 2021), Pages: 1-8


 Original Research Paper

 Title: Classification methods comparison for customer churn prediction in the telecommunication industry

 Author(s): Moh Makruf 1, *, Arif Bramantoro 2, Hasan J. Alyamani 3, Sami Alesawi 3, Ryan Alturki 4


 1Faculty of Information Technology, Universitas Budi Luhur, Jakarta, Indonesia
 2School of Computing and Informatics, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei
 3Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia
 4Department of Information Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia

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 * Corresponding Author. 

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The need for telecommunication services has increased dramatically in schools, offices, entertainment, and other areas. On the other hand, the competition between telecommunication companies is getting tougher. Customer churn is one of the areas that each company gains more competitive advantage. This paper proposes a comparison of several classification methods to make a prediction whether the customers cancel the subscription to a telecommunication service by highlighting key factors of customer churn or not. The comparison is non-trivial due to the urgent requirements from the telecommunication industry to infer the most appropriate techniques in analyzing their customer churn. This comparison is often of huge commercial value. The result shows that Artificial Neural Network (ANN) can predict churn with an accuracy of 79%, Support Vector Machine (SVM) with 78% accuracy, Gaussian Naïve Bayes, and K-Nearest Neighbor (KNN) with 75% accuracy, while Decision Tree with 70% accuracy. Moreover, the technique with the highest F-Measure is Gaussian Naïve Bayes with 65% and the technique with the lowest one is Decision Tree with 49%. Hence, ANN and Gaussian Naïve Bayes are two methods with high recommendation to predict the customer churn in the telecommunication industry. 

 © 2021 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Customer churn, Decision tree, Artificial neural network, Gaussian Naïve Bayes, Support vector machine, K-nearest neighbor

 Article History: Received 16 April 2021, Received in revised form 13 July 2021, Accepted 9 September 2021


No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


 Makruf M, Bramantoro A, and Alyamani HJ et al. (2021). Classification methods comparison for customer churn prediction in the telecommunication industry. International Journal of Advanced and Applied Sciences, 8(12): 1-8

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