YOLOv11 Model as a Smart Solution for Waste Identification and Classification in Automated Waste Management System
Dublin Core
Title
YOLOv11 Model as a Smart Solution for Waste Identification and Classification in Automated Waste Management System
Subject
YOLOv11, computer vision, machine learning, waste classification, waste management system
Description
Urbanization and population growth present significant challenges for efficient and sustainable waste management. This research develops an IoT-based intelligent system for waste classification and
management utilizing RFID technology, ESP32, a camera, an ultrasonic sensor, and the YOLOv11
object detection model. The system accurately identifies three categories of waste: organic, inorganic, and hazardous. The classification process is automated, incorporating user identification via RFID, servo-controlled bin lid operation, and capacity monitoring through an ultrasonic sensor. Data management is facilitated through a mobile application and a website, which provide user guidance and support for administrators. Test results indicate that the system achieves an average accuracy of 87.5% in the mAP50-95 evaluation, with specific accuracies of 89.0% for inorganic waste, 86.0% for hazardous waste, and 87.0% for organic waste. Despite these results, challenges remain, including object detection errors related to background interference. Future research should focus on enhancing the dataset and implementing data encryption to improve model accuracy and information security. This system demonstrates significant potential for enhancing waste management efficiency and promoting
sustainable environmental practices.
management utilizing RFID technology, ESP32, a camera, an ultrasonic sensor, and the YOLOv11
object detection model. The system accurately identifies three categories of waste: organic, inorganic, and hazardous. The classification process is automated, incorporating user identification via RFID, servo-controlled bin lid operation, and capacity monitoring through an ultrasonic sensor. Data management is facilitated through a mobile application and a website, which provide user guidance and support for administrators. Test results indicate that the system achieves an average accuracy of 87.5% in the mAP50-95 evaluation, with specific accuracies of 89.0% for inorganic waste, 86.0% for hazardous waste, and 87.0% for organic waste. Despite these results, challenges remain, including object detection errors related to background interference. Future research should focus on enhancing the dataset and implementing data encryption to improve model accuracy and information security. This system demonstrates significant potential for enhancing waste management efficiency and promoting
sustainable environmental practices.
Creator
Muhammad Fajar Jati Permana, Julio Caesar Ray Bakar Gani, Naufal Ahmad Fauzan, Anugrah Adiwilaga
Source
DOI: http://dx.doi.org/10.21609/jiki.v18i2.1490
Publisher
Faculty of Computer Science
Date
2025-06-26
Contributor
Sri Wahyuni
Rights
ISSN : 2502-9274
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Muhammad Fajar Jati Permana, Julio Caesar Ray Bakar Gani, Naufal Ahmad Fauzan, Anugrah Adiwilaga, “YOLOv11 Model as a Smart Solution for Waste Identification and Classification in Automated Waste Management System,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/9880.