<![CDATA[Waste sorting remains a persistent challenge in school environments due to the predominance of manual practices and inconsistent behavioral compliance, while the integration of simple and affordable technological solutions that simultaneously support learning objectives remains limited. This study aims to develop and evaluate an automated waste sorting system based on an Arduino microcontroller that integrates a color sensor (TCS3200) and a gas sensor (MQ-135), and to assess its performance and educational relevance within a school context. An experimental engineering research design was employed to design, assemble, and test a multi-sensor waste sorting prototype capable of classifying waste into organic and inorganic categories using combined visual and chemical indicators. A total of 40 waste samples, consisting of 20 organic and 20 inorganic items, were tested under controlled environmental conditions following sensor calibration to determine optimal threshold values. System performance was evaluated based on classification accuracy, reliability, and misclassification analysis. The results indicate that the integrated multi-sensor system achieved an overall classification accuracy of 92.5%, outperforming single-sensor approaches based solely on color or gas detection, and significantly reducing misclassification rates for visually ambiguous waste items. Reliability testing further demonstrated consistent classification outcomes across repeated trials. The novelty of this study lies in the integration of low-cost multi-sensor technology within an educationally oriented waste management system, positioning the developed device not only as a functional sorting tool but also as a STEM-based learning medium that supports contextual and project-based learning. These findings suggest that simple multi-sensor systems can effectively enhance waste sorting accuracy while contributing to sustainable environmental education in school settings. Â ]]>
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