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All Journal Journal of Intelligent Systems Technology and Informatics
Fauzi, Dhika Restu
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Computer Science & IT Control & Systems Engineering Engineering

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Comparison of CNN Models Using EfficientNetB0, MobileNetV2, and ResNet50 for Traffic Density with Transfer Learning Fauzi, Dhika Restu; Haqdu D, Gezant Ashabil
Journal of Intelligent Systems Technology and Informatics Vol 1 No 1 (2025): JISTICS Vol. 1 No. 1 March 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i1.6

Abstract

Traffic congestion in urban areas poses a significant and widespread challenge, stemming from the essential role of modern transportation in daily human activities. To address this issue, artificial intelligence (AI), particularly through applying convolutional neural networks (CNN), offers a promising solution for developing automated, accurate, and efficient traffic density classification systems. However, the performance of such systems is critically dependent on the selection of optimal model architecture. This study comprehensively analyzes three leading pre-trained CNN models: EfficientNetB0, MobileNetV2, and ResNet50. Utilizing a transfer learning approach, the models were trained over 20 epochs to classify traffic density into five categories: Empty, Low, Medium, High, and Traffic Jam. The research methodology was based on the public Traffic Density Singapore dataset. To enhance model robustness and address class imbalances, the initial dataset of 4,038 images was expanded to 6,850 images through data augmentation techniques. All images were subsequently resized to a uniform size of 224x224 pixels. The evaluation results conclusively demonstrate that the ResNet50 architecture delivered superior performance, achieving a validation accuracy of approximately 85%. Furthermore, ResNet50 consistently yielded higher precision, recall, and f1-score values across most classes. For comparison, EfficientNetB0 and MobileNetV2 achieved 81% and 79% validation accuracies, respectively. This study concludes that ResNet50 is the optimal architecture for this classification task, and these findings establish a foundation for developing real-world, intelligent traffic monitoring systems.
From Local Features to Global Context: Comparing CNN and Transformer for Sundanese Script Classification Agustiansyah, Yoga; Fauzi, Dhika Restu
Journal of Intelligent Systems Technology and Informatics Vol 1 No 2 (2025): JISTICS Vol. 1 No. 2 July 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i2.38

Abstract

The digital preservation of historical writing systems like Aksara Sunda is critical for cultural heritage, yet automated recognition is hindered by high character similarity and handwriting variability. This study systematically compares two dominant deep learning paradigms, Convolutional Neural Networks (CNNs) and Transformers, to evaluate the crucial trade-off between model accuracy and real-world robustness. Using a transfer learning approach, we trained five models (ResNet50, MobileNetV2, EfficientNetB0, ViT, and DeiT) on a balanced 30-class dataset of Sundanese script. Performance was assessed on a standard in-distribution test set and a challenging, independently collected Out-of-Distribution (OOD) dataset designed to simulate varied real-world conditions. The results reveal a significant performance inversion. While EfficientNetB0 achieved the highest accuracy of 96.9% on in-distribution data, its performance plummeted on the OOD set. Conversely, ResNet50, despite being lower in in-distribution accuracy, proved to be the most robust model, achieving the highest accuracy of 92.5% on the OOD data. This study concludes that for practical applications requiring reliable performance, the generalization capability demonstrated by ResNet50 is more valuable than the specialized accuracy of EfficientNetB0, offering a crucial insight for developing robust digital preservation tools for historical scripts.
Co-Authors Agustiansyah, Yoga Haqdu D, Gezant Ashabil