Dewi, Christine and Susanto, Rafael Andi and Chernovita, Hanna Prillysca and Santoso, Melati Viaeritas Vitrieco and Chen, Abbott Po Shun (2025) Evaluation of garbage detection efficacy utilising deep learning. Journal of Information and Communication Technology, 24 (4). pp. 36-57. ISSN 1675-414X

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Abstract

Garbage management has become an urgent global challenge due to the expected 70% increase in volume between 2025 and 2050, driven by rapid urbanisation and population growth. Low public understanding of garbage sorting and source-based disposal is reflected in Indonesia’s inefficient garbage processing. In addition to being ineffective, traditional methods like landfill disposal and incineration present serious environmental hazards. These traditional methods are often ineffective due to resource constraints and time-consuming nature, and cannot be relied upon for extended periods because they damage the environment during the process. To increase the effectiveness and precision of garbage management, this research proposes a garbage detection and classification model using YOLOv11. This research includes data collection and pre-processing, model training, and performance evaluation using metrics such as mean average precision (mAP). This research uses the Trashnet Garbage Classification Dataset, which has 2,524 total images that are divided into six categories. The key technical contributions of this research are to apply additional techniques, such as data augmentation strategies, to the dataset, enabling a comparison between the original and more advanced datasets. The purpose of the data augmentation technique is to improve model generalisation. The results of the evaluation metrics show that the model using the augmented dataset has slightly better performance with an mAP50 value of 97.8% than the model using the original dataset. This model is capable of identifying and classifying accurately all of the categories in the dataset.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, deep learning, garbage classification, object detection, YOLOv11
Subjects:	L Education > L Education (General)
Divisions:	School of Technology Management & Logistics
Depositing User:	Mdm. Rozana Zakaria
Date Deposited:	03 Nov 2026 12:43
Last Modified:	03 Nov 2026 12:43
URI:	https://repo.uum.edu.my/id/eprint/34479

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