Inorganic Waste Detection Application Using Smart Computing Technology with YOLOv8 Method

Abstract

Waste and renewable energy are critical issues in Indonesia, with the government aiming for renewable energy (RE) to contribute 23% to the national energy mix by 2025. This research focuses on developing a waste processing system through the TOSS (Tempat Olah Sampah Setempat) method and Peuyeumisasi technique to convert waste into biomass, such as briquettes and pellets for fuel. However, manual waste sorting remains time-consuming, prompting the need for a real-time detection system. You Only Look Once (YOLO) is an object detection approach that utilizes Convolutional Neural Networks (CNN) for object detection, making it one of the applications of intelligent computing in the field of computer vision. the latest version of YOLO is YOLO v8 offering several improvements over the previous version, can be employed in a real-time detection system to separate organic and inorganic waste. In this study, the dataset used consists of 2.000 images comprising five classes of inorganic waste: plastic bottles, plastic, glass, cans, and Styrofoam. The study demonstrates that YOLOv8 performs exceptionally well in detecting inorganic waste, with an average accuracy of 98% based on direct testing, and model evaluation showing an average accuracy of 99.33%, precision of 99.63%, recall of 96.53%, and an f1-score of 98.03%. These results indicate that the YOLOv8 method can significantly accelerate and simplify the waste sorting process, thereby supporting the conversion of waste into renewable energy. This research is expected to provide a practical solution and serve as a reference for future studies.