<![CDATA[High school students often face challenges in choosing a career due to guidance processes that remain conventional, inefficient, and impersonal. To provide a more objective and scientifically sound career recommendation solution, this study focuses on the design and implementation of a Decision Support System (DSS) built on a web-based platform. The system integrates Holland’s personality theory (RIASEC) with the Profile Matching method to compare students’ interest scores against the ideal profiles of six career alternatives. The algorithm’s operation begins by measuring the distance between a student’s actual profile and the career profiles established as ideal standards. This distance value is then converted into systematically determined weight scores. The final stage involves aggregating the Core Factor and Secondary Factor scores, with a 60% weight for the Core Factor and 40% for the Secondary Factor, which then yields a final score as the basis for ranking. The system was implemented using Laravel, React, and PostgreSQL, and tested on 32 students. Validation was conducted by comparing the system’s recommendations with expert psychological assessments. Test results showed a 78.13% level of agreement, where the majority of recommendations aligned with the students’ dominant personality characteristics. These findings demonstrate that the integration of computational approaches and career psychology theory can enhance the efficiency, transparency, and accuracy of the guidance process, while also serving as a strategic tool for counselors in data-driven student potential mapping.]]>
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