Fit Track: A Gym Member Monitoring System with Predictive Analytics for Almo Fitness Gym

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.

FitTrack, Predictive Analytics, Decision Tree Algorithm, Gym Member Monitoring, QR Code Attendance, ISO 25010, Agile Methodology, Web-Based System

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