<![CDATA[Population growth and urbanization have increased waste generation and intensified global waste management challenges. In Indonesia, national waste generation reached 38.4 million tons in 2023, with 38.38% remaining inadequately managed. This study aims to develop an Artificial Intelligence of Things (AIoT)-based system for automatic waste classification and real-time bin capacity monitoring. The system integrates the YOLOv8n model to identify four waste categories (organic, inorganic, hazardous/B3, and others) with ultrasonic sensors for capacity measurement, coupled with a web platform for data visualization. Model evaluation yielded a Macro F1-Score of 63.9%, with the best performance in the organic class (91.33%), followed by inorganic (68.37%), and hazardous/B3 (31.92%). Ultrasonic sensors demonstrated a near-linear relationship between waste height and capacity percentage (4.5% per cm), validating their reliability for real-time monitoring. The developed system proves the feasibility of AIoT integration for automated waste sorting, although further optimization is required to improve classification accuracy for minority classes. This research contributes to the development of intelligent solutions supporting more efficient and sustainable urban waste management.]]>
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