<![CDATA[This study creates an online Decision Support System (DSS) using the TOPSIS algorithm to fairly choose outstanding teachers from vocational schools in Bekasi City, which has 87 state and private vocational secondary schools with about 62,000 students. To tackle the current biased selection process, our research uses a multi-criteria approach that looks at discipline (25%), travel costs (20%), personality (20%), teaching administration (15%), and learning achievements (20%). Targeting this substantial educational population, our research addresses the current subjective selection process by implementing a multi-criteria approach evaluating discipline (25%), travel costs (20%), personality (20%), teaching administration (15%), and learning achievements (20%). The TOPSIS method was selected for its proven effectiveness in ranking alternatives based on geometric distance from ideal solutions, particularly valuable in large-scale educational contexts. Analysis of 14 teacher candidates from SMK Bina Karya Mandiri demonstrated the system's precision, with Didi Saputra, S.Pdi, emerging as top-ranked (preference value: 0.63). When extrapolated to Bekasi's 87 SMKs, the model shows potential to standardize teacher assessment citywide, reducing regional disparities in recognition practices. The web-based platform enhances accessibility, allowing principals across 21 sub-districts to input localized data while maintaining centralized benchmarking. Key findings reveal (1) discipline and personality collectively account for 45% of exemplary status determination, (2) cost-related factors show inverse correlation with remote school nominations, and (3) system implementation could reduce selection time by ≈68% compared to manual methods. This study contributes both a scalable framework for educational DSS and empirical data on vocational teacher excellence criteria in urban Indonesia.]]>
