<![CDATA[This study develops a Decision Support System (DSS) to assist in selecting the best employees by combining the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. Manual employee evaluations often result in subjectivity and bias, potentially impacting fairness and strategic HR planning. Therefore, a structured and objective evaluation system is crucial to enhance decision-making accuracy. AHP is applied to determine the weight of each evaluation criterion through pairwise comparisons and consistency analysis, ensuring reliable and valid weight values. These weights are then used in the SAW method to normalize employee performance scores and compute the final rankings. The DSS is built using the Extreme Programming (XP) methodology, emphasizing iterative development and active user feedback to ensure usability and functionality. The evaluation process is based on five benefit-type criteria: Innovative, Creative, Experimental, Agile, and Visionary. Results indicate that Employee 2 achieved the highest final score of 95.58, and was selected as the best employee. Black-box testing was conducted to validate the system’s functionality, and all modules such as employee data input, criteria management, score computation, and ranking display performed correctly. This DSS promotes fairness, transparency, and accountability in performance evaluation and provides a scalable framework that can adapt to organizational needs. Future enhancements may include integrating data visualization and expanding criteria dynamically. Overall, the system supports strategic human resource decisions and ensures objective evaluations through a reliable and systematic approach.]]>
