Electronic Waste Recognition and Classification Using YOLOv8 for ICT Sector.

This study develops an image recognition system to classify electronic waste (e-waste), especially the e-waste from information and communication technology (ICT) sector. From an organizational perspective, sustainable ICT e-waste management is very important due to environmental impact as well as data security and privacy compliance. This study utilizes the deep learning model YOLOv8 to recognize electronic wastes originated from a higher educational institution within the umbrella of computer accessories, batteries, storage devices, networking devices, and printed circuit boards. The dataset consists of 1690 images, divided into 13 classes, each containing 130 images. The training and validation process was carried out using Google Colab. The result shows that the model successfully classifies each type of e-waste, achieving mean Average Precision (mAP) of 95%. The results of this study will be further used to design smart bins for e-waste collection and planning at different organizations.