Decision Support System for Selecting the Best Employee Using the Simple Additive Weighting Method

Employee performance appraisal is a crucial aspect of human resource management, as it influences strategic decisions such as promotions, rotations, and incentives. However, manual evaluations are often prone to subjectivity and inefficiencies in terms of time and effort. This study aims to design and implement a decision support system (DSS) using the Simple Additive Weighting (SAW) method to determine the best employee objectively and measurably. The research adopts a software engineering approach with the waterfall model through stages of requirement analysis, system design, implementation, testing, and maintenance. The developed system is web-based and incorporates five key criteria: productivity, loyalty, work attitude, team contribution, and innovation. The testing results indicate that the system can process employee data, compute preference values, and display final rankings accurately and consistently with manual calculations. The system is also equipped with result export features and a user-friendly interface that facilitates the evaluation process. This study contributes a digital tool that reduces subjectivity in performance assessments and improves HR operational efficiency. In conclusion, the implementation of the SAW method in a web-based system is proven effective for supporting multi-criteria decision-making in selecting the best employee and is suitable for dynamic work environments.