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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Engineering and Technology (INAJET) Journal of Informatics and Computer Science (JINACS) Jurnal Ilmiah Edutic : Pendidikan dan Informatika J-Icon : Jurnal Komputer dan Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ervin Yohannes
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Informatics and Computer Science (JINACS)

 1 
Semantic Segmentation Using the U-Net Architecture on Monocular Datasets Ahmad Fikri Hanafi; Ervin Yohannes
Journal of Informatics and Computer Science (JINACS) Vol. 7 No. 01 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v7n01.p37-42

Abstract

Abstract— This study implements a deep learning model based on the U-Net architecture with a pre-trained ResNet50 backbone on ImageNet to solve the task of semantic segmentation on monocular images. The Cityscapes dataset is used as the main benchmark because it provides high-quality data with high resolution that is widely recognized in urban image segmentation research. Experiments were conducted to evaluate the model's performance with varying learning rate values, aiming to understand the model's sensitivity to training parameters. The results show that a learning rate of 1e-4 yields optimal performance, achieving a Mean Intersection over Union (Mean IoU) of 86.59% and pixel accuracy of 97.63%. Visualization of the segmentation predictions demonstrates the model's ability to accurately recognize urban objects and structures, especially under varying lighting conditions and background complexity. These findings confirm the effectiveness of U-Net in image segmentation tasks, as well as the importance of hyperparameter selection and dataset quality in achieving high model performance in the monocular image domain.   Keywords— Convolusional Neural Network, Deep Learning, U-Net, Encoder-Decoder, Semantic Segmentation
Pre-Trained Convolutional Neural Network Benchmark For Multi-Class Weather Modeling Ramadhany, Sinta Dhea; Yohannes, Ervin
Journal of Informatics and Computer Science (JINACS) Article In Press(1)
Publisher : Universitas Negeri Surabaya

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Abstract

Abstract— Weather forecasting plays a crucial role in reducing the risks of extreme events that threaten human safety, economic stability, and the environment. Traditional forecasting methods relying on manual observation have developed into modern approaches using satellite, radar, and computational models; however, prediction accuracy remains limited due to the complexity of atmospheric systems and data constraints. In this context, deep learning, particularly Convolutional Neural Networks (CNNs), provides significant potential for automatic weather classification through digital imagery. This study evaluates and compares the performance of four pre-trained CNN architectures VGG16, ResNet50, AlexNet, and InceptionV3 on the Kaggle “Multi-class Weather Dataset,” which contains 860 images categorized into four classes: Cloudy, Shine, Rain, and Sunrise. The methodology involves data augmentation, fine-tuning, and systematic experimentation with various hyperparameters and data split ratios to enhance model generalization. The evaluation metrics applied include accuracy, precision, recall, and F1-score. Experimental results reveal that InceptionV3 outperforms other models, achieving up to 98% training accuracy and 96% validation accuracy due to its effective multi-scale feature extraction and regularization. ResNet50 delivers balanced results with validation accuracy up to 94%, while AlexNet records relatively high detection counts but lower overall performance. In contrast, VGG16 yields the lowest accuracy among the tested models. These findings highlight InceptionV3 as the most robust architecture for weather image classification and emphasize the importance of model selection in balancing prediction accuracy and computational efficiency. The study contributes as a foundation for the development of deep learning-based weather recognition systems that can support early warning applications and disaster risk reduction. Keywords— Convolutional Neural Network, Weather Classification, ResNet50, VGG16, AlexNet, InceptionV3
Co-Authors Ahmad Fikri Hanafi Asyrofa Rahmi, Asyrofa Esti, Esti Yogiyanti Mulyono, Alfito Nafisah, Nurun Putri, Windy Chikita Cornia Ramadhany, Sinta Dhea Shih, Timothy K. Utaminingrum, Fitri Wayan Firdaus Mahmudy Wiyli Yustanti Yogiyanti, Esti YUNI YAMASARI