Implementation of convolutional neural network in image-based waste classification

A'yun, Adila Qurrota, Suhartono, Suhartono and Lestari, Tri Mukti (2025) Implementation of convolutional neural network in image-based waste classification. Journal of Applied Informatics and Computing (JAIC), 9 (4). pp. 1778-1784. ISSN 2548-6861

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Abstract

The increasingly complex issue of waste management, particularly in the sorting process, demands efficient and accurate technology-based solution. This study aims to implement the Convolutional Neural Network (CNN) method for image-based waste classification, focusing on two classes paper and plastic. The dataset used consists of 2000 images, with an 80% proportion for training and 20% for testing. This study tested four scenarios combining image augmentation and classification methods, namely threshold and one-hot encoding, and evaluated model performance using accuracy, precision, recall, and F1-score metrics. The best results were obtained in the scenario using image augmentation with the one-hot encoding classification method, with an accuracy of 89%, precision of 88.5%, recall of 89%, and F1-score of 88.5%. These findings indicate that implementation of CNN can enhance the effectiveness of image-based waste classification and support recycling efforts through a smarter and more automated sorting system.

Item Type:	Journal Article
Keywords:	Convolutional Neural Network; Image Classification; Waste
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080101 Adaptive Agents and Intelligent Robotics
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Dr Suhartono M.Kom
Date Deposited:	03 Sep 2025 09:57

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