<![CDATA[This study develops a decision support system to recommend microcontroller and IoT learning devices for schools, universities, and training centers under realistic budget constraints while considering both technical capability and educational suitability. The alternatives are grouped into three budget tiers and evaluated using nine criteria covering price, CPU frequency, Flash, RAM/PSRAM, connectivity, usable GPIO, ease of learning, learning resources/community, and local availability/warranty. Objective criterion weights are computed using the Entropy method, and tier-wise rankings are produced using Additive Ratio Assessment (ARAS) through utility scores relative to an ideal alternative. Indicative local price and availability information are compiled from Tokopedia, while qualitative criteria are scored using consistent rubrics to support reproducibility. The results identify ESP32-CAM + baseboard as the top recommendation in Tier 1, LILYGO T-Display S3 in Tier 2, and M5StickC Plus2 in Tier 3; across tiers, Entropy assigns the largest weights to the most discriminative criteria, particularly RAM/PSRAM and, in higher tiers, Flash. The study is limited by market price volatility, approximations in usable GPIO values, and rubric-based qualitative scoring, and it also reflects the tendency of Entropy to concentrate weights on highly dispersed criteria, potentially amplifying outlier advantages. Overall, the proposed tier-aware Entropy–ARAS framework provides a transparent and actionable approach for educational institutions to justify device procurement and usage decisions based on budget, functionality, and learning readiness.]]>
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