<![CDATA[This study implements the WASPAS method to improve the selection process of outstanding students at Battuta University. Traditional evaluation methods often suffer from subjectivity and inconsistency when assessing multiple criteria, such as academic performance, research contributions, leadership, and community service. The WASPAS method addresses these limitations by systematically integrating the Weighted Sum Model and Weighted Product Model, ensuring a balanced and transparent ranking system. Using a quantitative descriptive approach, this research evaluates 10 shortlisted students based on five weighted criteria: GPA (0.35), research publications (0.25), leadership (0.20), community service (0.12), and competition achievements (0.08). The results show that WASPAS produces a reliable composite score (Qi), with the top-ranked student (S5) achieving a score of 0.888. Sensitivity analysis confirms the robustness of the rankings, as variations in criterion weights (±20%) only minimally affected the top candidates. Compared to Battuta University’s existing manual evaluation system, WASPAS enhances objectivity, traceability, and fairness by reducing human bias. The study highlights the potential of WASPAS as a decision-support tool in higher education, particularly for merit-based selections. Future research could expand this framework to scholarship allocations, faculty evaluations, or adaptive weighting systems using machine learning. By adopting WASPAS, universities can promote data-driven, transparent, and holistic student assessments, ultimately fostering academic excellence and institutional credibility.]]>
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