<![CDATA[The scholarship selection process involves multiple academic, social, and economic criteria, making decision-making increasingly complex. Manual selection procedures are prone to subjectivity, inconsistency, and limited transparency, potentially reducing the accuracy of scholarship allocation. This study aims to develop a hybrid Decision Support System (DSS) by integrating the Bayesian Theorem and the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to improve the objectivity and efficiency of scholarship recipient selection. The Bayesian Theorem is employed in the initial stage to classify candidates into eligible and ineligible groups based on posterior probabilities, while MOORA is applied to rank eligible candidates through normalization, weighting, and optimization processes. The study used data from 20 scholarship applicants evaluated using eight criteria: academic achievement, non-academic achievement, community involvement, economic condition, parents' income, residence in underdeveloped, frontier, and outermost (3T) areas, disability status, and the number of family dependents. The classification results identified 14 candidates as eligible, while 6 candidates were excluded from the ranking stage, reducing the number of alternatives processed during optimization by 30%. Subsequently, the MOORA method generated the priority ranking of scholarship recipients based on the optimization value of each alternative. The findings demonstrate that the integration of the Bayesian Theorem and MOORA provides a more objective, transparent, and efficient scholarship selection process than conventional manual approaches. The proposed model can serve as an effective decision support tool for scholarship selection in higher education institutions.]]>
