Improved roommate matching residential support system: Design and implementation of UniMatch

This study presents the design and implementation of UniMatch, an improved web-based student residential support system developed to address inefficiencies in traditional university housing management. Conventional accommodation systems rely on manual or semi-digital processes that consider only basic criteria such as gender and academic level, resulting in poor roommate compatibility, administrative delays, and reduced student satisfaction. The aim of this study is to develop a user-centred platform that enhances roommate matching and streamlines residential management through the integration of lifestyle-based compatibility profiling and digital automation. An incremental software development methodology was adopted, utilizing React.js for the frontend, Node.js with Express.js for backend services, and PostgreSQL for database management. The Theory of Planned Behavior (TPB) was applied as the theoretical framework to understand and predict student adoption behavior. The UniMatch system incorporates a swipe-based roommate matching interface driven by compatibility scores derived from lifestyle questionnaires, real-time chat between matched students, maintenance request tracking, administrative dashboards, and announcement management. System evaluation through unit, integration, and user acceptance testing demonstrated improved roommate compatibility outcomes, enhanced communication between students and administrators, and reduced operational delays compared to traditional methods. Users reported ease of navigation and clarity of interface design during acceptance testing. The study concludes that integrating behavioral and lifestyle factors into student housing systems significantly enhances the residential experience and operational efficiency in university environments. Future work should focus on expanding the system to multiple institutions, incorporating artificial intelligence for enhanced matching accuracy, and strengthening data privacy and security mechanisms.