<![CDATA[The Competency Test Learning Guidance is a platform for students or nurses who wish to enter the workforce. The learning guidance organized by Appskep Indonesia is held annually in several periods. One of the efforts to improve the quality of the participants is by evaluating them and selecting the best participants for each class period in Appskep Indonesia. The selection of the best participants in the Competency Test Learning Guidance at Appskep Indonesia currently lacks a system capable of conducting effective and efficient evaluations. The criteria for selecting the best participants in the Competency Test Learning Guidance at Appskep include the average exam scores from the Competency Test tryouts taken by the participants, access to materials available to all registered participants, attendance at the start and end of the classes, and each participant's level of activeness. The decision support system for selecting the best participants for this research uses the Weighted Product (WP) and Simple Additive Weighting (SAW) methods, implemented in a website. SAW is a method that involves finding the weighted sum of performance ratings for each alternative across all attributes. In contrast, WP uses multiplication to relate attribute ratings, where each attribute is first raised to the power of its respective weight. The research was conducted to test three class periods starting from July 2023 to March 2024. The study compared the results of the methods in Excel and on the website, achieving 100% accuracy. This research compares the SAW and WP methods for the intensive batch 158, intensive batch 157, and intensive batch 156 class periods. The results for the intensive batch 158 showed that the best participant was Aditya Rizal with WP and SAW scores of 0.01207 and 1.01, respectively. For the intensive batch 157, the best participant was Sarri Qurrotul with WP and SAW scores of 0.01099 and 0.95, respectively. For the intensive batch 156, the best participant was Ari Lani with WP and SAW scores of 0.01707 and 1.01, respectively]]>
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