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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Bauravindah, Achmad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Lightweight Models for Real-Time Steganalysis: A Comparison of MobileNet, ShuffleNet, and EfficientNet Bauravindah, Achmad; Fudholi, Dhomas Hatta
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6091

Abstract

In the digital age, the security of communication technologies is paramount, with cybercrime projected to reach $10.5 trillion annually by 2025. While encryption is vital, decrypted data remains vulnerable, prompting the exploration of steganography as an additional security layer. Steganography conceals data within digital media, but its misuse for cyberattacks—such as embedding malware—has highlighted the need for steganalysis, the detection of hidden data. Despite extensive research, few studies have explored lightweight deep learning models for real-time steganalysis in resource-constrained environments like mobile devices. This research evaluates MobileNet, ShuffleNet, and EfficientNet for such tasks, using the BOSSbase-1.01 dataset. Models were assessed based on accuracy, computational efficiency, and resource usage. MobileNet achieved the highest computational speed but with only 63.8% accuracy, falling short of practical application. ShuffleNet and EfficientNet performed at random-guessing levels with 50% accuracy, reflecting the challenges of steganalysis on mobile platforms. Future work aims to improve accuracy by integrating advanced preprocessing techniques, attention mechanisms, and hybrid architectures, as well as leveraging ensemble methods for improved detection. Data augmentation, transfer learning, and hyperparameter tuning will also be explored to optimize model performance. This study contributes by identifying these challenges and offering insights for future research, focusing on optimizing models and preprocessing techniques to enhance detection accuracy in resource-constrained environments.
Efficient Thoracic Abnormalities Detection Using Mobile Deep Learning Models Bauravindah, Achmad; Fudholi, Dhomas Hatta; Wahyuningrum, Rima Tri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2268

Abstract

Indonesia faces a critical shortage of radiologists, with only 1.2 radiologists per 100,000 individuals. This shortage leads to delays in diagnosing thoracic abnormalities such as pneumothorax, cardiomegaly, nodule/mass, consolidation, and infiltration. Chest X-ray (CXR) interpretation remains challenging due to overlapping radiological features, necessitating AI-assisted solutions. This study evaluates three lightweight deep learning models—MobileNetV2, ShuffleNetV2, and EfficientNetB0—for automated thoracic abnormality detection using the ChestX-ray8 dataset. We assessed model performance using accuracy, precision, recall, F1-score, and AUC-ROC, selecting the best model based on the highest per-fold F1-score. EfficientNetB0 emerged as the top-performing model, achieving a macro-average F1-score of 0.556 and AUC-ROC of 0.765, outperforming MobileNetV2 (0.494, 0.719) and ShuffleNetV2 (0.481, 0.713). Grad-CAM analysis revealed strong localization for pneumothorax and consolidation but misclassifications in cardiomegaly and nodule/mass detection due to poor feature differentiation. The findings highlight EfficientNetB0’s potential as an AI-assisted diagnostic tool for low-resource settings while also underscoring the need for segmentation-based pretraining and multi-scale feature extraction to enhance detection accuracy. Future work should focus on optimizing sensitivity to subtle abnormalities and ensuring clinical trust through improved interpretability techniques.
Classification of Betel Leaf Diseases Based on Convolutional Neural Network to Increase Production Herbal Spice Materials Tri Wahyuningrum, Dr. Rima; Hamed Ayani, Irham; Bauravindah, Achmad; Siradjuddin, Indah Agustien; Faradisa, Irmalia Suryani
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4653

Abstract

Traditional medicine is the practice of utilizing medicinal plants to treat various illnesses, passed down from generation to generation. In Indonesia, there are various traditional medicines, one of which is using green betel leaves. One part of the green betel plant that is commonly attacked by pests is the leaf. The Convolutional Neural Network (CNN) method is a very common method used for image classification because this method produces the highest accuracy in classification and pattern recognition. This research uses data totaling 4000 images which are divided into four classes: healthy green betel leaves, anthracnose green betel leaves, bacterial spot betel leaves, and healthy red betel leaves. Detecting the disease type facilitates farmers in acknowledging the necessary measures required to provide treatment. Therefore, this study utilizes the benefits of the CNN approach, specifically its capability to conduct precise object detection and classification in image data, to minimize the widespread of disease. The CNN architectures implemented are DenseNet201, EfficientNetB3V2, InceptionResNetV2, MobileNetV2 and XceptionResnet50V2. Based on our research, the InceptionResNetV2 model achieved the highest performance with an accuracy of 86.0%, loss of 0.3880, and ROC of 98.0%. In the other hand, the MobileNetV2 and EfficientNetV2B3 models suffered from overfitting and underfitting and the models failed to classify betel leaf diseases.
Co-Authors Fudholi, Dhomas Hatta Hamed Ayani, Irham Irmalia Suryani Faradisa Rima Tri Wahyuningrum Siradjuddin, Indah Agustien Tri Wahyuningrum, Dr. Rima