Fit Track: A Gym Member Monitoring System with Predictive Analytics for Almo Fitness Gym
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Abstract: A Web-Based Gym Member Monitoring System Using Predictive Analytics and Decision Tree Algorithm focuses on enhancing gym operations and member management through data-driven automation. The system integrates QR code attendance tracking, automated payment processing, and predictive analytics to improve efficiency, accuracy, and decision-making at Almo Fitness Gym in Taguig City. The Linear Regression algorithm in the Almo Fitness Gym system can predict future attendance trends and revenue based on members’ historical usage patterns. Logistic Regression helps classify members as active or at risk of cancellation by analyzing their visit frequency and payment behavior. Meanwhile, Random Forest and K-Means Clustering enhance predictive analytics by improving the accuracy of retention forecasts and grouping members into segments for targeted fitness programs and personalized promotions. The platform’s web-based interface allows both administrators and members to access real-time information on attendance, payments, and progress tracking. The development process followed the Agile methodology, ensuring flexibility, user feedback integration, and iterative improvements across system modules. Evaluation was conducted using ISO 25010 software quality standards, focusing on functionality, reliability, usability, efficiency, and security. Results from 50 respondents comprising 30 gym users and 20 IT experts showed that the system performed excellently in reliability, efficiency, and security, confirming its effectiveness and user-friendliness. Users found the interface intuitive and responsive, while experts validated its compliance with standard software design principles. Findings demonstrated that predictive analytics and the Decision Tree algorithm are effective in optimizing gym operations, supporting data-informed decisions, and personalizing fitness management. Overall, FitTrack serves as both a technological and operational solution that transforms manual gym processes into an intelligent, automated system. The study recommends further development through mobile integration, offline functionality, and enhanced security measures to ensure scalability, accessibility, and long-term system sustainability.
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Hadawale, A., Pawar, P., Bhajnawale, P., & Patange, A. (2025). Web-based gym management platforms for improved user experience and operational productivity. International Journal of Computer Applications, 227(4), 15–22.
Kim, J., & Park, S. (2021). Cloud-based fitness tracking system with wearable integration. Journal of Healthcare Engineering, 2021, Article 8856123. https://doi.org/10.1155/2021/8856123
Olofernes, M., Dela Cruz, A., & Santos, R. (2021). Web-based gym management system for Groovy Fitness Gym. Philippine Journal of Information Systems, 14(2), 45–56.
Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4, 77–90. https://doi.org/10.1613/jair.279
Sharma, P., Abhishek, A., & Esha, R. (2022). Development of MyGym Website: A web-based gym management system. International Research Journal of Engineering and Technology, 9(6), 1256–1263.
Suryawanshi, A., Bokare, N., Bhadre, S., & Lahankar, R. (2024). Web-based gym management system using XAMPP and MySQL. International Journal of Advanced Research in Computer Science, 15(1), 88–94.
Villanueva, K., Sevilla, M., & Verecio, C. (2025). QR-code-based management system for Olympic Fitness Gym. Asia Pacific Journal of Information Technology, 8(2), 66–75.
Zhang, H., Li, Q., & Wu, Y. (2023). Smart fitness tracking system using cloud computing and machine learning. Journal of Computer Applications in Sport Science, 12(4), 221–235.
Zhao, J., Wang, X., & Zhu, L. (2023). Design and implementation of a gym management system based on JSP and MySQL. International Journal of Information Systems and Technology, 21(3), 55–62.
Sobreiro, P. (2021). Predicting fitness centre dropout. PubMed Central (PMC). https://pmc.ncbi.nlm.nih.gov/articles/PMC8508547/
Marqués-Jiménez, D. (2024). A Random Forest clustering to explore the influence of physical fitness level of youth basketball players on match-related physical performance. SAGE Journals. https://journals.sagepub.com/doi/abs/10.1177/17543371231200056
Van der Zanden, M. (2023). The prediction of membership churn in a large gym club chain (Master’s thesis). Arno Repository, Tilburg University.https://arno.uvt.nl/show.cgi?fid=172644
Niu, Z. (2025). Customer segmentation and management strategy using K-Means clustering for gym customers. ScitePress https://www.scitepress.org/Papers/2024/132140/132140.pdf
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