<![CDATA[Elective course selection is a critical academic decision in higher education; however, students frequently rely on peer conformity rather than systematic self-assessment, resulting in competency–course mismatches and suboptimal academic outcomes. Existing decision support systems have addressed this problem algorithmically, yet few have integrated psychological interest profiling within a usability-evaluated web-based platform. This study employed a Research and Development (R&D) design with the waterfall development model across four phases: needs analysis, system design, implementation, and testing. A web-based elective course recommendation system — integrating a psychological interest assessment as the recommendation engine — was developed using PHP, MySQL, Laravel, and Bootstrap. Usability was evaluated by 10 purposively selected students using the System Usability Scale (SUS), and data validity was assessed through Pearson correlation analysis. The system achieved a mean SUS score of 81.3, classifying it within the "Good to Excellent" category (Grade B). All 10 SUS items returned Pearson correlation coefficients exceeding the critical r-table value of 0.576, confirming full instrument validity. These findings demonstrate that a psychologically grounded, web-based recommendation system can attain above-benchmark usability, supporting its potential as a scalable academic advising supplement. The study contributes an empirically validated model integrating psychological profiling with usability-centered system design — an approach underexplored in prior educational recommender systems literature.]]>
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