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All Journal Jurnal ULTIMATICS
Deas Aghellar
Universitas Nusa Putra
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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the Comparative Analysis and Optimization of CNN and MobileNetV2 Using Data Augmentation and Fine-Tuning for Waste Classification Deas Aghellar; Aswan Supriyadi Sunge
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4687

Abstract

Waste management has become an increasingly critical environmental issue that requires effective technology-based solutions. One promising approach is the application of deep learning methods for automatic waste classification. This study aims to compare the performance of Convolutional Neural Network (CNN) and MobileNetV2 models, as well as to evaluate the impact of data augmentation and optimization techniques on model performance. The dataset consists of 2,527 waste images categorized into six classes: plastic, paper, glass, metal, cardboard, and trash. The training process incorporates data augmentation, transfer learning, and optimization techniques such as dropout, learning rate adjustment, and early stopping. Experimental results show that the CNN model achieves a validation accuracy of 48.5%, whereas MobileNetV2 attains 69.8%. Furthermore, MobileNetV2 demonstrates better generalization performance, indicated by a smaller gap between training and validation accuracy. These findings suggest that MobileNetV2 combined with appropriate optimization techniques is more effective than CNN for waste classification tasks. Keywords— CNN, Data Augmentation, MobileNetV2, Transfer Learning, Waste Classification
Co-Authors Aswan Supriyadi Sunge