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All Journal Jurnal EECCIS Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Journal of Applied Smart Electrical Network and System (JASENS) Bulletin of Community Engagement Jurnal Polimesin Jurnal Cakrawala Maritim International Journal of Mechanical, Electrical and Civil Engineering International Conference on Maritime Technology and Its Application PROCEEDINGS CONFERENCE ON DESIGN AND MANUFACTURE AND ITS APPLICATION
Agus Khumaidi
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Religion Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : jurnal eeccis

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Implementation of YOLOv5s for Automatic Waste Category Classification in Digital Waste Bank Systems Rinanto, Noorman; Mat Syai’in; Agus Khumaidi; Muhammad Khoirul Hasin; Lilik Subiyanto; Vivin Setiani; Firstama Yusuf Noor; Harun Ismail
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1894

Abstract

The increasing volume of organic waste in campuses or households demands innovative solutions for waste management and classification. This study proposes an automated classification system based on deep learning using the YOLOv5s algorithm to detect 14 categories of inorganic waste in real-time. The dataset consists of over 3.500 labeled images, annotated via Makesense.ai and augmented using Roboflow. The model was trained on Google Collaboratory for 100 epochs using the YOLOv5s architecture and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP). Training result show mAP@0.5 approaching 100% and mAP@0.5:0.95 around 85%, with an average confidence score of 88.30% during real-time testing using a webcam. These findings demonstrate that YOLOv5s can accurately and efficiently classify waste objects, offering strong potential for integration into digital waste bank systems to enhance the efficiency and transparency of waste management processes.
Co-Authors , Rini Indarti A. Dewana Putra Abdulloh Sami Alfaris Adam Maulana, Adam Maulana Adhitya, Ryan Yudha Adi, Muhammad Adimassyah Adianto Alfianto Taufiqul Malik Anggarjuna Puncak Pujiputra Arfianto, Afif Zuhri Arigo, Mohammad Arigo Al. Hafid As'ari, Nur Hasyim As'ari Asri, Purwidi Deni Almunawar Dhika Aditya Purnomo Didi, Perdinan Setia Budi Didik Sukoco Dimas Pristovani R Dimas Pristovani Riananda Dimas Prostovani Riananda Dwi Sasmita Aji Pambudi Dzil, Fadhli Evi Nafiatus Sholikhah Fipka Bisono Firstama Yusuf Noor Fitri Hardiyanti Fonda Jiwa Arkananta Tusila Gusti Audryadmaja, Anugerah Ekha Hannisa Kautsarani Hamidah Harun Ismail Hasin, Muhammad Khoirul Hendro Agus Widodo Ii Munadhif Imam Sutrisno Isa Rachman Joessianto Eko Poetro Joko Endrasmono Joko Endrasmono4 Lilik Subiyanto M. Dhani Inwanul F M. Dhifa Alfitra M. Fakhri Ali Mufidz Mat Syai'in Mat Syai’in Mochamad Dhani Inwanul Farikh Mochamad Yusuf Santoso Mochammad Madhoni Setiawan Mochammad Rifai Mohamad Hakam Mohammad Abu Jamii’in Mohammad Basuki Rahmad Mohammad Basuki Rahmat Muhammad Abu Jami’in Muhammad Khoirul Hasin Nurahmad Hadi Cahyadi Nurissabiqoh Binta Bayu Ramadhan, Novalliansyah Ramadhan Riko Satrya Fajar Jaelani Putra Rinanto, Noorman Rini Indarti Rizal Indrawan Rohmansyah, Ade Fitra Ryan Yudha Adhitya Semeru Fatkhurrozi A Setiyono, Dwi Agus Shivy Aura Adhyatma Sholahuddin Muhammad Irsyad Sofi Berliana Rizky Tusila, Fonda Jiwa Arkananta Tusila Vivin Setiani Wahyudi, Asyal Achnan Yaqin, Muhammad Ainul Yaqin Yuning Widiarti Zainu Rafsanjani Zhafirah, Talitha Amalia Zindhu Maulana A.P. Zindhu Maulana Ahmad Zindhu Maulana Ahmad Putra