SLMS: Development of a Science Laboratory Management System with Analytics Using the Apriori Algorithm

Abstract

This study developed a Science Laboratory Management System (SLMS) with analytics using the Apriori Algorithm to improve science laboratory resource management and support data-driven decision-making. The study used Agile Software Development methodology to guide the system’s design, development, testing, and evaluation. The system developed modules for user management, equipment and chemical inventory, borrowing and return transactions, report generation, and an analytics dashboard. Historical laboratory transaction data were processed using the Apriori Algorithm to identify frequent itemsets and association rules among laboratory equipment, materials, chemicals, and activities. The developed system was evaluated using the ISO/IEC 25010 Software Quality Model in terms of functional suitability, performance efficiency, usability, reliability, security, maintainability, and portability. Results demonstrated that the system effectively automated key laboratory management processes and generated meaningful usage patterns that could support inventory monitoring, procurement planning, and resource allocation. The system received an overall evaluation interpreted as excellent, indicating its acceptability and suitability for laboratory operations. The findings suggest that integrating association rule mining into science laboratory management can enhance operational efficiency and provide actionable insights for administrators. Future enhancements may include predictive analytics, barcode or RFID integration, mobile and cloud deployment, and interoperability with institutional information systems.