Optimizing Waste Classification Model using YOLOv11 Architecture - Wisesa, Bradika Almandin; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Rahmat Lionza (International Journal of Informatics and Computation, 2025)
Municipal solid waste management remains a critical challenge due to rapid urbanization and consumption patterns. This study proposed a image based waste classification model for organic, inorganic, and hazardous (B3) waste using the YOLOv11 architecture. To conduct the study, we gathered a huge dataset of 5,000 images across daylight, dusk, and night conditions. According to experimental results, the proposed model can achieve an mAP@0.5 of 70%, a precision of 69%, a recall of 70%, and an F1-score of 0.70, operating at 43 frames per second (FPS) with 102 GFLOPs. It can confirm its suitability for real-time applications in resource-constrained environments. Compared to heavier deep learning models, this efficiency-performance balance highlights the practical advantage of YOLOv11 for continuous waste monitoring and automated sorting systems.