<![CDATA[The success of an institution is highly dependent on the quality of its human resources (HR). Employee performance evaluation is essential in determining strategic decisions such as promotions, mutations, and competency improvement. However, the manual evaluation process often faces obstacles such as delays and human error. This research aims to develop a decision support system (SPK) to select the best employees by applying the analytical hierarchy process (AHP) and simple additive weighting (SAW) methods. The research method used is the System Development Life Cycle (SDLC), which includes the stages of planning, needs analysis, system design, implementation, testing, and maintenance. Testing is carried out by Black Box Testing, White Box Testing, and acceptance testing by users (acceptance test). Based on the tests conducted using data per semester from all employee activities in performance assessment, the author concludes that the appropriate method based on the dataset used from the comparison of the use of the AHP and SAW methods in the selection of the best employees at the Teacher Professional Education Agency of the State University of Surabaya is the AHP method which gets results of 100% precision, 84% recall, 84% accuracy, and 91% of F1 scores. The results show that the methods and algorithms used can produce the best alternative recommendations for the Teacher Professional Education Agency of the State University of Surabaya. This study concludes that implementing AHP-based SPK can increase objectivity and efficiency in selecting the best employees. The implications of this research are expected to help institutions manage employee performance more systematically and accurately, as well as encourage the improvement of the quality of human resources in the academic environment.]]>
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