Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia

  • Dewi Lestari Mu'ti Universitas Mercu Buana Yogyakarta, Indonesia
  • Putri Taqwa Prasetyaningrum Universitas Mercu Buana Yogyakarta, Indonesia
DOI: 10.51519/journalisi.v7i2.1120
Keywords: Gym churn prediction, fitness industry, Random Forest vs SVM vs XGBoost, customer retention, machine learning for gyms

Abstract

The fitness industry in Yogyakarta is experiencing rapid growth accompanied by intense competition among gym service providers. This has led to an increase in membership churn, negatively impacting business sustainability. This study aims to conduct a comparative analysis of three supervised classification algorithms Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to predict member churn at EightGym Indonesia. The dataset, consisting of 1,287 membership records collected between July 2024 and April 2025, includes features such as visit frequency, subscription duration, membership type, and churn status. The study focuses on predicting members at risk of subscription cancellation using historical data such as visit frequency, subscription duration, membership type, and churn status. The methodology follows the CRISP-DM framework, covering business understanding, data preparation, modeling, evaluation, and deployment stages. Evaluation results indicate that XGBoost delivers the best performance with 95% accuracy, high recall, and F1-score, making it the most effective algorithm for churn prediction in this context. Additionally, the model was implemented in a web-based prototype application to support gym management decision-making. The findings contribute significantly to the application of machine learning for customer retention strategies in the fitness industry and provide a foundation for the future development of predictive decision support systems.

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References

R. L. Siegel, T. B. Kratzer, A. N. Giaquinto, H. Sung, and A. Jemal, “Cancer statistics, 2025.,” CA. Cancer J. Clin., no. October 2024, pp. 10–45, 2025, doi: 10.3322/caac.21871.

H. M. Bizuayehu et al., “Global Disparities of Cancer and Its Projected Burden in 2050.,” JAMA Netw. open, vol. 7, no. 11, p. e2443198, 2024, doi: 10.1001/jamanetworkopen.2024.43198.

K. Kesehatan and R. Indonesia, “Rencana kanker nasional 2024-2034,” no. September, 2024.

V. Davalos and M. Esteller, “Cancer epigenetics in clinical practice,” CA. Cancer J. Clin., vol. 73, no. 4, pp. 376–424, 2023, doi: 10.3322/caac.21765.

N. Khan, F. Afaq, and H. Mukhtar, “Lifestyle as risk factor for cancer: Evidence from human studies,” Cancer Lett., vol. 293, no. 2, pp. 133–143, 2010, doi: 10.1016/j.canlet.2009.12.013.

P. Marino et al., “Healthy Lifestyle and Cancer Risk: Modifiable Risk Factors to Prevent Cancer,” Nutrients, vol. 16, no. 6, pp. 1–28, 2024, doi: 10.3390/nu16060800.

E. Weiderpass, “Lifestyle and cancer risk,” J. Prev. Med. Public Heal., vol. 43, no. 6, pp. 459–471, 2010, doi: 10.3961/jpmph.2010.43.6.459.

R. Sharman, Z. Harris, B. Ernst, D. Mussallem, A. Larsen, and K. Gowin, “Lifestyle Factors and Cancer: A Narrative Review,” Mayo Clin. Proc. Innov. Qual. Outcomes, vol. 8, no. 2, pp. 166–183, 2024, doi: 10.1016/j.mayocpiqo.2024.01.004.

T. J. Gumilang and B. Hernawan, “The Benefits of Physical Activity to Reduce Mortality in Postmenopausal Breast Cancer Patients: A Literature Review,” Indones. J. Cancer, vol. 17, no. 1, p. 73, 2023, doi: 10.33371/ijoc.v17i1.917.

J. Semrl and A. Matei, “Churn prediction model for effective gym customer retention,” in Proc. 2017 Int. Conf. Behavioral, Economic, Socio-cultural Computing (BESC), 2017, pp. 1–3.

C. Yeomans, A. Karg, and J. Nguyen, “Who churns from fitness centres? Evidence from behavioural and attitudinal segmentation,” Manag. Sport Leis., vol. 0472, pp. 1–17, 2024, doi: 10.1080/23750472.2024.2305896.

M. M. S. Nurhidayat and Dyah Anggraini, “Analysis and Classification of Customer Churn Using Machine Learning Models,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 6, pp. 1253–1259, 2023, doi: 10.29207/resti.v7i6.4933.

M. Imani, A. Beikmohammadi, and H. R. Arabnia, “Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels,” Technologies, vol. 13, no. 3, pp. 0–39, 2025, doi: 10.3390/technologies13030088.

R. Krishna, D. Jayanthi, D. S. Shylu Sam, K. Kavitha, N. K. Maurya, and T. Benil, “Application of machine learning techniques for churn prediction in the telecom business,” Results Eng., vol. 24, no. August, p. 103165, 2024, doi: 10.1016/j.rineng.2024.103165.

X. Li and Z. Li, “A hybrid prediction model for e-commerce customer churn based on logistic regression and extreme gradient boosting algorithm,” Ing. des Syst. d’Information, vol. 24, no. 5, pp. 525–530, 2019, doi: 10.18280/isi.240510.

J. Maan and H. Maan, “Customer Churn Prediction Model using Explainable Machine Learning,” arXiv, vol. 11, no. 1, pp. 33–38, 2023.

C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A Comparative Analysis of XGBoost,” arXiv, pp. 1–20, 2019, doi: 10.1007/s10462-020-09896-5.

M. R. Khan, J. Manoj, A. Singh, and J. Blumenstock, “Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty,” Proc. - 2015 IEEE Int. Congr. Big Data, BigData Congr. 2015, pp. 677–680, 2015, doi: 10.1109/BigDataCongress.2015.107.

S. M. Sina Mirabdolbaghi and B. Amiri, “Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions,” Discret. Dyn. Nat. Soc., vol. 2022, 2022, doi: 10.1155/2022/5134356.

A. Dhini and M. Fauzan, “Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company,” Int. J. Technol., vol. 12, no. 5, pp. 1030–1037, 2021, doi: 10.14716/ijtech.v12i5.5223.

C.-F. Tsai and Y.-H. Lu, “Customer churn prediction by hybrid neural networks,” Expert Syst. Appl., vol. 36, no. 10, pp. 12547–12553, 2022.

A. U. Haspriyanti and P. W. Prasetyaningrum, “Penerapan Data Mining Untuk Prediksi Layanan Produk Indihome Menggunakan Metode K-Nearst Neighbor Arwa,” Inf. Syst. Artif. Intell., vol. 20, no. 2, pp. 100–107, 2021.

D. Ruswanti, D. Susilo, and R. Riani, “Implementasi CRISP-DM pada Data Mining untuk Melakukan Prediksi Pendapatan dengan Algoritma C.45,” Go Infotech J. Ilm. STMIK AUB, vol. 30, no. 1, pp. 111–121, 2024, doi: 10.36309/goi.v30i1.266.

R. Wirth and J. Hipp, “CRISP-DM: towards a standard process model for data mining. Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, 29-39,” Proc. Fourth Int. Conf. Pract. Appl. Knowl. Discov. Data Min., no. 24959, pp. 29–39, 2000.

C. Lukita, L. D. Bakti, U. Rusilowati, A. Sutarman, and U. Rahardja, “Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction,” J. Appl. Data Sci., vol. 4, no. 4, pp. 454–465, 2023, doi: 10.47738/jads.v4i4.131.

Published
2025-06-30
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How to Cite
Mu’ti, D., & Prasetyaningrum, P. (2025). Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia. Journal of Information Systems and Informatics, 7(2), 1592-1611. https://doi.org/10.51519/journalisi.v7i2.1120
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Vol 7 No 2 (2025): June
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