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All Journal CommIT (Communication & Information Technology) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Ilvico Sonata
Universitas Bina Nusantara, Jakarta, Indonesia
Computer Science & IT

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End-to-End Steering Angle Prediction for Autonomous Car Using Vision Transformer Ilvico Sonata; Yaya Heryadi; Antoni Wibowo; Widodo Budiharto
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8425

Abstract

The development of autonomous cars is currently increasing along with the need for safe and comfortable autonomous cars. The development of autonomous cars cannot be separated from the use of deep learning to determine the steering angle of an autonomous car according to the road conditions it faces. In the research, a Vision Transformer (ViT) model is proposed to determine the steering angle based on images taken using a front-facing camera on an autonomous car. The dataset used to train ViT is a public dataset. The dataset is taken from streets around Rancho Palos Verdes and San Pedro, California. The number of images is 45,560, which are labeled with the steering angle value for each image. The proposed model can predict steering angle well. Then, the steering angle prediction results are compared using the same dataset with existing models. The experimental results show that the proposed model has better accuracy regarding the resulting MSE value of 2,991 compared to the CNN-based model of 5,358 and the CNN-LSTM combination model of 4,065. From the results of this experiment, the ViT model can replace the existing model, namely the CNN model and the combination model between CNN and LSTM, in predicting the steering angle of an autonomous car.
Comparative Analysis of Adversarial Robustness Across CNN, VGG19, and Vision Transformer Architectures Using Multiple Defense Strategies Yaya Heryadi; Ilvico Sonata; Bambang Krismono Triwijoyo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6433

Abstract

This study aims to systematically evaluate the adversarial robustness of Convolutional Neural Network (CNN), VGG19, and Vision Transformer (ViT) architectures for chest X-ray classification. Specifically,this study aims to determine the relative influence of model architectures and defense strategies under identical experimental conditions The research method: All three architectures were evaluated using a chest X-ray dataset under white-box attacks using the Fast Gradient Sign Method (FGSM) and the Iterative Fast Gradient Sign Method (IFGSM), with perturbation levels ranging from ε = 0.00 to 0.30. Five defense strategies-Adversarial Training, Adversarial Distillation, TRADES, MART, and Adversarial Weight Perturbation (AWP)-were compared to a baseline without defenses within a unified experimental framework. Differences between defense methods were assessed using the Friedman test. The results showed that without defenses, all models experienced substantial performance degradation, with IFGSM causing a more severe degradation than FGSM. Under FGSM attacks, VGG19 demonstrated the most consistent resilience across defense strategies. Under IFGSM attacks, ViT combined with Adversarial Training achieved the strongest resilience. Defense effectiveness varied across architectures, and no single defense consistently performed best. Friedman’s test identified significant differences between defense methods for FGSM and IFGSM attacks across all architectures (p < 0.001). The study concluded that adversarial resilience is largely determined by the interaction between model architecture and defense strategy. Defense selection has a greater impact on resilience than architecture complexity alone, highlighting the need for architecture-specific defense selection when developing secure medical image classification systems.
Co-Authors Antoni Wibowo Bambang Krismono Triwijoyo Widodo Budiharto Yaya Heryadi Yaya Heryadi