Klasifikasi Citra Sampah Botol Plastik Jenis HDPE dan PET Menggunakan Algoritma YOLOv7 - Opita Purwasih; Wijang Widhiarso; Muhammad Rizky Pribadi (Bulletin of Computer Science Research, 2025)
The classification of plastic bottle waste, particularly High Density Polyethylene (HDPE) and Polyethylene Terephthalate (PET), remains a challenge in recycling processes due to their similar visual characteristics. Misclassification can lead to a decline in recycled material quality and economic losses in the waste management industry. This research aims to develop an automated image-based classification system to distinguish between HDPE and PET plastic waste using the You Only Look Once version 7 (YOLOv7) object detection algorithm. The dataset consists of plastic bottle images in various physical conditions, annotated with bounding boxes to support model training. The data were split into 70% for training, 20% for validation, and 10% for testing. The best performance was achieved with a batch size of 16 and 100 training epochs, resulting in a precision of 93.9%, recall of 91.6%, and a mean Average Precision (mAP@0.5) of 96.5%. The model demonstrated the ability to accurately classify both types of plastic bottles, even when objects were deformed. These results suggest that the YOLOv7 algorithm is highly capable for implementation in image-based waste classification systems, enhancing sorting efficiency and supporting more sustainable plastic waste management practices.