Data-Driven Decision Support System for Scholarship Selection Based on Multi-Criteria Analytics - Sumiran Sumiran; Solly Aryza; Zulham Sitorus (SEAN Institute, 2026)
Scholarship selection is a strategic process in higher education institutions that requires fairness, transparency, and objective decision-making. However, conventional selection methods often face challenges in handling multiple assessment criteria and large volumes of applicant data. This study proposes a Data-Driven Decision Support System (DSS) for scholarship selection in Deli Serdang Regency based on Multi-Criteria Analytics. The system integrates academic and non-academic indicators, including Grade Point Average (GPA), family income, academic achievements, organizational involvement, attendance records, and extracurricular activities. A quantitative approach was employed using student data collected from higher education institutions in Deli Serdang. Multi-criteria analysis was applied to evaluate and rank scholarship candidates according to predefined weighting schemes. The results demonstrate that the proposed system effectively identifies eligible candidates while improving consistency, transparency, and decision accuracy. Furthermore, the data-driven framework reduces subjective bias and supports evidence-based scholarship allocation. The study concludes that the proposed DSS can serve as a reliable tool for educational institutions and local governments in optimizing scholarship selection processes and promoting equitable access to educational opportunities.