Optimizing Human Resource Selection Through TOPSIS-Based Multi-Criteria Decision Making
Abstract
Human resource quality is a pivotal determinant of organizational success, yet recruitment often suffers from subjectivity and inefficiency. This study addresses challenges in applicant mapping and assessment bias by implementing a Decision Support System (DSS) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The research utilizes four selection criteria based on standard Indonesian recruitment frameworks : General Intelligence Test (30%), National Insight Test (10%), fild ability test (20%), and Interview Performance (40%). Methodologically, the TOPSIS method was employed to rank candidates based on their geometric distance from the positive and negative ideal solutions. Results demonstrate that the TOPSIS-based DSS achieved 90% alignment with historical corporate hiring decisions. Furthermore, the system improved decision-making effectiveness by 98.78%, accelerating the overall recruitment timeline by 30%. This study contributes to the field of HR technology by providing a scalable, objective framework for multi-criteria candidate evaluation. By integrating mathematical rigor into personnel selection, the proposed system minimizes human error and optimizes organizational efficiency in the Indonesian corporate context.
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