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All Journal Building of Informatics, Technology and Science
Sugiyanto Sugiyanto
Universitas Dian Nuswantoro, Semarang
Computer Science & IT

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Pemodelan Pola Temporal Action Unit untuk Pengenalan Ekspresi Wajah Berbasis Bidirectional LSTM Muhammad Ghozali Sulton; Sugiyanto Sugiyanto
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9315

Abstract

This study develops a facial expression recognition system based on Facial Action Units (AU) data using a Bidirectional Long Short-Term Memory (BiLSTM) model. The dataset consists of AU data obtained from a supervisor, sourced from DCAP-SWOZ (USC Institute for Creative Technologies), a multimodal corpus containing AU values extracted from human interaction videos. A total of 188 AU files were used in this research. Initial labeling was performed using Facial Action Coding System (FACS)-based rules as pseudo-labels serving as a starting point for training the BiLSTM model. This approach was chosen because the dataset lacks inherent emotion labels, necessitating a label initialization mechanism. The BiLSTM model functions as a temporal smoother designed to reduce noise and label inconsistencies that commonly occur in frame-by-frame rule-based approaches. The trained model then performs inference on the same data to generate final labels with improved temporal stability. Evaluation was conducted by measuring model consistency against FACS rules and qualitative analysis of temporal stability in generated labels. Data were processed into 30-frame sequences with a 1-frame sliding window to effectively capture expression dynamics patterns. The BiLSTM model was trained using two hidden layers with dropout regularization. Evaluation results showed 96.61% consistency against FACS rules with high performance across all emotion classes, including anger (99.11%), disgust (97.98%), fear (94.08%), happiness (99.29%), neutral (96.42%), sadness (98.31%), and surprise (99.16%). Qualitative analysis demonstrated that the model successfully reduced frame-by-frame label fluctuations by 73% compared to pure rule-based approaches, producing more stable and realistic emotion segmentation. These results demonstrate that the combination of FACS-based labeling and the BiLSTM model can produce a temporally consistent automated labeling system capable of accelerating labeled dataset creation, although validation against human ground truth remains necessary as future research.
Deep Fake Image Detection Using Vision Transformer with Random Oversampling Technique Dipo Paudro Tirto Prakoso; Sugiyanto Sugiyanto
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9316

Abstract

Recent developments in deep learning have facilitated the generation of visually convincing deepfake images, creating serious concerns for the reliability and security of digital media content. The primary challenge lies in detecting these sophisticated manipulations while handling imbalanced datasets, a common issue in deepfake detection research. This research focuses on designing a robust deepfake image classification model based on the Vision Transformer (ViT) architecture to differentiate between authentic and manipulated images. The main objectives are to: (1) adapt and fine-tune a pre-trained Vision Transformer for binary classification, (2) evaluate the effectiveness of Random Oversampling in addressing class imbalance while preventing data leakage, and (3) assess model performance using comprehensive metrics. Methods: A pre-trained Vision Transformer model (Deep-Fake-Detector-v2-Model) was adapted and fine-tuned using a dataset consisting of 190,335 images. To overcome the issue of class imbalance, a Random Oversampling strategy was applied exclusively to the training set after dataset splitting to prevent data leakage. The dataset was divided into training and testing subsets using an 80:20 ratio. During the training phase, data augmentation techniques such as image rotation, sharpness variation, and pixel normalization were employed. The model was trained for four epochs with a learning rate of 1×10⁻⁶ and a batch size of 32. Results: Experimental evaluation demonstrates that the proposed model achieves a classification accuracy of 94.46% on the test dataset. The model demonstrates high precision of 97.56% for fake images and 91.74% for real images, with corresponding recall rates of 91.21% and 97.72% respectively. The F1-score reaches 94.46% for both classes, indicating balanced performance. Novelty: This research presents a novel application of Vision Transformer architecture for deepfake detection, combining efficient transfer learning with strategic oversampling to handle imbalanced datasets while preventing data leakage. The study demonstrates that ViT-based models can effectively capture subtle manipulation artifacts in deepfake images, achieving superior performance compared to traditional convolutional neural network approaches.
Comparative Analysis of VGG16 Transfer Learning Fine-Tuning Strategies for Automated Concrete Crack Classification Adwinof Akmal Juantoro; Sugiyanto Sugiyanto
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9468

Abstract

Identifying cracks in concrete structures is critical for structural health monitoring, as undetected cracks can lead to catastrophic infrastructure failure. Conventional manual inspections are labour-intensive, subjective, and costly, necessitating automated solutions capable of consistent and scalable deployment. This paper presents a systematic comparative study of four VGG16 transfer learning strategies for automated binary classification of concrete surface cracks. VGG16 was selected for its proven effectiveness in binary image classification tasks, well-established pre-trained feature representations from ImageNet, and low trainable parameter count that reduces overfitting risk on domain-specific datasets. A dataset of 40,000 concrete surface photographs was utilised, divided 80:20 for training and validation. Four training configurations were evaluated: Baseline CNN, Full Freeze, Partial Fine-Tuning, and Full Fine-Tuning, all trained using the Adam optimiser (learning rate 0.001), binary cross-entropy loss, and early stopping. Partial Fine-Tuning achieved the highest accuracy at 99.90%, followed by Full Freeze (99.84%) and Baseline CNN (99.69%). Full Fine-Tuning collapsed to 50.00% due to catastrophic forgetting. The best-performing Partial Fine-Tuning configuration achieved an AUC of 0.9998, precision of 0.9990, recall of 0.9990, and F1-score of 0.9990, with only 15 misclassifications out of 8,000 validation samples. These results confirm that Partial Fine-Tuning is the recommended strategy for concrete crack classification in structural health monitoring application.
Co-Authors Adwinof Akmal Juantoro Dipo Paudro Tirto Prakoso Muhammad Ghozali Sulton