Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Muhammad Fauzan Adzim
Universitas Lambung Mangkurat
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Waste Identification Using a Hybrid Convolutional Neural Network and Vision Transformer on Visually Heterogeneous Images Muhammad Fauzan Adzim; Dewi Sri Susanti; Sigit Dwi Prabowo
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In image classification, convolutional neural networks (CNNs) focus on local patterns, whereas vision Transformers (ViTs) emphasize global context. Combining the two in a hybrid CNN-ViT model may yield a more comprehensive image representation. Waste image classification with visually heterogeneous characteristics can be used to effectively evaluate the performance of the hybrid CNN-ViT model. In addition, such classification supports the crucial need for accurate waste-type identification to enable effective waste management systems. This study investigates a hybrid CNN-ViT model for classifying 24705 organic and recyclable waste images. The workflow involves resizing, an 80:10:10 split, and data augmentation, with models trained for 50 epochs using BCE loss and the Adam optimizer. Evaluation is conducted at the best epoch, defined as the epoch with the highest validation accuracy. For comparison, CNN and ViT models are also trained and evaluated separately. On the test set, the hybrid CNN-ViT model achieves an accuracy of 91.54%, the CNN achieves 91.78%, and the ViT achieves 87.17%. These findings show that CNNs provide an effective and efficient baseline, while the hybrid CNN-ViT model delivers performance competitive with CNNs and is worth considering as a robust alternative for image-based waste classification tasks.
Co-Authors Dewi Sri Susanti Sigit Dwi Prabowo