<![CDATA[The increasing variety of laptop products with diverse specifications and prices has made the selection process more challenging for students. Different academic activities such as coding, gaming, graphic design and editing, 3D engineering modeling, and multitasking require distinct hardware specifications, making it difficult to determine the most suitable laptop manually. This study aims to develop an Android-based Decision Support System (DSS) for laptop recommendations using the Simple Additive Weighting (SAW) method. The system evaluated 25 laptop alternatives based on nine evaluation criteria, namely price, RAM, CPU, GPU, SSD, battery, device weight, screen size, and body material. The SAW method was implemented through decision matrix construction, normalization, weighted preference calculation, and alternative ranking. The results showed that different user profiles produced different recommendation outcomes. The ASUS Zenbook 14 OLED UM3402YA achieved the highest preference value in the coding and multitasking categories, while the Nitro V obtained the highest score in the gaming, graphic design and editing, and 3D engineering modeling categories. The developed Android application provides users with a practical and flexible tool for obtaining laptop recommendations according to their specific needs. The findings indicate that the SAW method is effective in generating objective and personalized laptop recommendations through a multi-criteria decision-making approach.]]>
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