Article Per Year (5 Year)
p-Index From 2020 - 2025
3.346
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electronics and Instrumentation Systems Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK Jurnal sistem informasi, Teknologi informasi dan komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Bulletin of Computer Science Research Jurnal Pengabdian kepada Masyarakat Bina Darma Algoritme Jurnal Mahasiswa Teknik Informatika Arcitech: Journal of Computer Science and Artificial Intelligence MDP Student Conference
Wijang Widhiarso
Universitas Multi Data Palembang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Computer Science Research

 1 
Klasifikasi Citra Sampah Botol Plastik Jenis HDPE dan PET Menggunakan Algoritma YOLOv7 Purwasih, Opita; Widhiarso, Wijang; Muhammad Rizky Pribadi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.654

Abstract

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.
Co-Authors Ahmad Farisi Andres, Yohanes Asyraq, Cerwyn Cendy Prakarsah Chandra Wijaya Dafid Dafid Derry Alamsyah Edi Winarko Edo Renaldo Ericco Andreas Ervina Patricia Arifin Hafiz Irsyad Kersen Kersen Khan, Zicola Vladimir VIky Kurniawan, Wiji Manalu, Josua Michael Michael Muhammad Irfan Muhammad Rachmadi, Muhammad Muhammad Rizky Pribadi Purwasih, Opita Ricky Putra Sardika Sardika, Ricky Putra SATRIAWAN, MUHAMMAD AKBAR Sri Hartati Usman Usman Vera Nofita Verren Oktaviana Wiwik Handayani Yuda, Andika Dirma