Implementation of a Smart Waste Sorting System for Automatic Waste Classification in Campus Areas (Study Case: Bandung Institute of Technology)

This study addresses the critical challenge of waste management at the Bandung Institute of Technology, where manual waste sorting is time-consuming, labor-intensive, and prone to errors due to confusion about waste categories. To improve efficiency and accuracy, an automated waste sorting system was developed using machine learning and Internet of Things (IoT) technology. The system, based on the YOLOv5n object detection algorithm, achieved high precision and performance in identifying and sorting waste. Additionally, a data management subsystem was implemented to monitor and optimize waste bin placement through cloud databases and a web interface. The testing results confirmed the system’s effectiveness and ease of use, offering a scalable solution for better waste management on campus.