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p-Index From 2020 - 2025
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This Author published in this journals
All Journal JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Engineering and Technology (INAJET) Journal of Informatics and Computer Science (JINACS)
Ervin Yohannes
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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 1 
Traffic Sign Recognition Using Detector-Based Deep Learning Method Mulyono, Alfito; Ervin Yohannes
Indonesian Journal of Engineering and Technology (INAJET) Vol. 7 No. 1 (2024): September 2024
Publisher : Fakultas Teknik Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajet.v7n1.p1-6

Abstract

Traffic is a key element in the transportation system. Traffic is an integral part of urban life and a key element in the transportation system. Traffic safety is a major concern to prevent accidents and ensure safe mobility. Traffic accidents are one of the most common occurrences. . But on the other hand, the increase in road accidents is increasing, which can be caused by people's lack of knowledge about traffic. The main solution to overcome this problem is to increase knowledge about traffic. The application of artificial intelligence, especially object detection methods with the use of detector-based deep learning methods, is one method that has proven efficient in detecting objects in real-time. In this research, object recognition is performed using SSD (Single Shot MultiBox Detector) where the model is trained and tested for its performance in detecting traffic signs in Indonesia. From the research results, the mAP 50 and mAP 50-95 values are 89.66% and 65.49%, respectively.  Keyword: Deep Learning, SSD, Traffic Signs.
Explainable Artificial Intelligence (XAI) for Identification of Using Obesity Factors Hybrid Artificial Neural Network Approach and SHapley Additive exPlanations Esti, Esti Yogiyanti; Yuni Yamasari; Ervin Yohannes
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p19-27

Abstract

This study aims to develop and evaluate an obesity classification model using an Artificial Neural Network (ANN) combined with Explainable Artificial Intelligence (XAI) techniques based on SHAP (SHapley Additive exPlanations). The model was trained and tested using two different optimizers, Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD), across multiple train-test ratios and epoch variations. The experimental results indicate that the Adam optimizer consistently outperformed SGD in terms of accuracy, loss value, and stability of evaluation metrics. The best performance was achieved with a 90:10 train-test ratio at 100 epochs, yielding an accuracy of 94.74%, a loss of 0.1899, precision, recall, and an f1-score of 0.95. To improve interpretability, SHAP was applied to identify the most influential features in the classification process. The analysis revealed that features such as Weight, Height, Gender, and Age significantly contribute to the model's predictions. Based on the SHAP interpretation, feature selection was conducted using the top nine features with the highest SHAP values. Retraining the ANN with these selected features resulted in improved performance, achieving 98.56% accuracy, a loss of 0.0638, and a precision, recall, and F1-score of 0.99 . These findings demonstrate that integrating XAI with ANN not only enhances transparency and interpretability but also boosts classification performance and computational efficiency. This approach shows strong potential for supporting decision-making in healthcare, particularly for early detection and intervention in cases related to obesity.
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
Co-Authors Ahmad Fikri Hanafi Esti, Esti Yogiyanti Mulyono, Alfito YUNI YAMASARI