Pendeteksian dan Klasifikasi Sampah pada Bank Sampah Berbasis Web Menggunakan YOLOv11

Abstract

The problem of poorly managed household waste management can increase the burden on the environment and reduce the effectiveness of recycling. Waste banks in general still rely on manual systems in sorting waste which is prone to errors and requires more labor. This research aims to develop a web-based waste detection and classification system using the You Only Look Once (YOLO) version 11 yolov11n (nano) method. The research method includes downloading a secondary dataset named R1 Test from the Roboflow Universe platform, collecting primary datasets from internet scraping and manual shooting which resulted in a total of 27,400 litter images with 9 (nine) different types, namely bottle, cans, cardboard, cup, foil, food, paper, paper_bag, and plastic. The results show that the yolov11n model is able to detect objects with sufficient accuracy and light computational resources by producing a precision value of 0.917, recall of 0.890, mAP50 of 0.932% and mAP50-95 of 0.758% in all classes. The best model results obtained are integrated into the web using the flask framework.