<![CDATA[The selection of laboratory assistants is a crucial process that requires decision-making based on various criteria, such as academic performance, activeness, technical skills, and communication abilities. However, manual processes often face challenges such as subjectivity and inefficiency. This study aims to develop a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to optimize the selection of laboratory assistants. We utilize the SAW method because it can integrate criterion weights and normalize data to produce objective preference values. We conducted a simulation using dummy data from 10 students and four criteria, applying weights determined through discussions with laboratory managers. The results show that student A5 achieved the highest preference score (1.000), reflecting optimal performance across all criteria. The developed system also demonstrated its ability to enhance the transparency, efficiency, and accuracy of the selection process. The implementation of this system offers a practical solution for managing student data and making fairer decisions, with potential applications in other selection contexts, such as scholarships or academic awards]]>
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