Decision Support Systems Utilization in Academic Administration

This paper presents a comprehensive analysis of the implementation and effectiveness of decision support systems (DSS) in academic administration, specifically within higher education institutions in collaboration with Sebelas Maret University (UNS). The research focuses on the design and development of a robust DSS that provides actionable recommendations in key administrative areas such as scholarship eligibility, student specialization placement, tuition fee classification, and optimization of institutional resources. The system integrates multiple decision-making methodologies, including simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), and analytic hierarchy process (AHP), to ensure accurate and datadriven outputs. Extensive testing was conducted to assess the system's performance and reliability. The SAW and TOPSIS methods demonstrated exceptional accuracy, with minimal error rates of 0.00001 and 0.00002, respectively, and high correlation coefficients, indicating strong consistency. While AHP showed a slightly higher error rate of 0.16497, it produced the closest Euclidean distance (0.43839) to the TOPSIS results, highlighting a strong alignment between methods. These findings confirm the system's capability to support complex decision-making processes and underline its potential to significantly improve academic administrative efficiency and effectiveness within higher education settings.