<![CDATA[The process of selecting new employees in Micro, Small, and Medium Enterprises (MSMEs) is often still carried out subjectively, which can lead to less optimal decision-making. This study aims to apply and compare the Simple Additive Weighting (SAW) and Weighted Product (WP) methods as decision support systems for new employee selection in MSMEs. The evaluation is conducted based on four criteria: education level, work experience, skill competency, and interview results. The dataset consists of ten job candidates that are processed through weight normalization, preference value calculation, and ranking stages. The results show that both methods are capable of providing objective and measurable recommendations for selecting the best employees, although differences appear in the final ranking of candidates because the SAW method calculates scores by summing weighted normalized values for each criterion, while the WP method multiplies each criterion value raised to its weight, making the influence of high or low scores more pronounced. The SAW method is simpler and easier to understand, while the WP method is more sensitive to criterion weights and better distinguishes candidates with varied performance levels. The best alternative tends to consistently rank at the top in both methods. Therefore, the implementation of the SAW and WP methods can assist MSMEs in making systematic and accurate employee selection decisions based on a dataset of ten candidates evaluated across four assessment criteria.]]>
Copyrights © 2026
