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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,113 Documents
 106   107   108   109   110 
Web-Based Deepfake Detection Using VERITAS: Integrating Vision-Based Excitation with Transformer-Driven Intelligence Alam Rahmatulloh; Surjono, Herman Dwi; Arifin, Fatchul; Gunawan, Rohmat; Rizal, Randi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7320

Abstract

This study proposes a web-based deepfake detection system that integrates Vision-Based Excitation technology and Transformer-based intelligence, called VERITAS (Vision-based Excitation and Robust Intelligence for Transformer-Assisted Deepfake Detection). The system is designed to automatically detect manipulated images and videos by leveraging the Vision Transformer (ViT) model architecture, equipped with the Grad-CAM mechanism for interpretability of detection results. The study conducted a series of tests to measure the system's performance in various scenarios and ensure its reliability in dealing with various types of input. Load testing results showed that up to 30 simultaneous users, the system can operate with good responsiveness (average response time of 130 ms) without experiencing errors. However, when the number of users reaches 40 or more, the system performance drops drastically with a very high error rate, reflecting limitations in handling server load. Real-world testing showed the system can detect deepfakes with an accuracy of 73.61%, with results varying depending on the quality of the tested images. Furthermore, unit functional testing and coverage analysis demonstrated an excellent test pass rate (85%), with all major functions running smoothly and error handling needed to be fixed in some code sections. Overall, the VERITAS system demonstrates strong potential for web-based deepfake detection, with high reliability under low load and adequate performance in functional testing. However, further optimization is needed to handle higher user loads.
Siamese Model-Based Face Verification Using CNN and MobileNetV2 Abd Rahman; Agus Mohamad Soleh; Erfiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6996

Abstract

Face verification plays an important role in computer vision, especially in mobile and embedded systems with limited computational capacity. This study proposes a face verification system based on the Siamese Neural Network (SNN) architecture by integrating six embedding models. These models consist of a standard CNN, an L2-normalized CNN, a baseline MobileNetV2, a structurally adjusted MobileNetV2, a pre-trained MobileNetV2, and a fine-tuned MobileNetV2. The dataset includes facial images captured from three webcams and additional samples obtained from the Labeled Faces in the Wild and ImageNet datasets. The experimental procedure includes image preprocessing, construction of balanced positive and negative image pairs, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that the pre-trained MobileNetV2 and the standard CNN achieve the highest verification accuracy, reaching 100 percent and 99.998 percent, respectively. Among all models, the structurally adjusted MobileNetV2 presents the best trade-off by combining high accuracy, computational efficiency, and training stability while successfully avoiding overfitting. The real-time implementation involves only the structurally adjusted MobileNetV2 model due to its lightweight structure and consistent performance. This model produces low embedding distances, low latency, and high throughput during CPU-based inference. The performance outperforms GPU execution in one-by-one image processing. The proposed system offers a practical and efficient face verification solution for deployment in identity authentication applications on resource-constrained platforms. These findings support the development of scalable and adaptive biometric security systems that rely on deep learning.
Weather and Marine Multi-output Prediction Using XGBoost on Automatic Weather Station Data Arifin, Willdan Aprizal; Anzani, Luthfi; Ma'ruf, M; Daud, Anton; Handyanto, Lukman; Maulidia, Raisa; Maulsyid, Ramzan Pradana; Fadzar, Angga
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7031

Abstract

Climate change on a global scale has triggered an increase in sea levels and heightened the frequency of extreme weather events, especially in maritime countries such as Indonesia. These conditions necessitate the development of accurate and adaptive weather and marine prediction systems. This study proposes a multi-output prediction model using the eXtreme Gradient Boosting (XGBoost) algorithm based on BMKG's Automatic Weather Station (AWS) data from the BMKG. The data cover the period 2022-2025 with high temporal resolution and include five main parameters: wind speed, water level, water temperature, relative humidity, and wind direction. The hyperparameter tuning process led to the discovery of an optimal configuration capable of enhancing the model's accuracy. The evaluation results of the coefficient of determination (R²) and Root Mean Squared Error (RMSE) metrics show that the model can predict water temperature, water level, and relative humidity with very high accuracy, which is more than 85 percent. The model also performed well in predicting wind speed, although it still faced difficulties in handling wind direction due to its cyclical nature. Overall, the XGBoost approach proved effective in modeling weather and marine parameters simultaneously and has the potential to be integrated into environmental monitoring systems in Indonesia's coastal and archipelagic regions.
GraphiBERT-ML: A Knowledge-Enhanced NER Approach for Cross-Domain Comparative Analysis of Machine Learning Literature Khouya, Nabila; Retbi, Asmaâ; Bennani, Samir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7160

Abstract

The exponential growth of scientific literature on platforms such as arXiv presents a major challenge in identifying and comparing key contributions to machine learning across diverse academic domains. To address this, we propose GraphiBERT-ML, a knowledge-enhanced extension of BERT that integrates semantic embeddings extracted from DBpedia to improve named entity recognition (NER) in scientific articles. To the best of our knowledge, this study presents the first knowledge-enhanced NER model that explicitly integrates DBpedia-based embeddings for large-scale cross-domain scientific analyses. The model was evaluated on a cross-domain dataset spanning eight fields, including computer science, physics, biology, finance, and economics. Experimental results show that GraphiBERT-ML achieves its highest performance in computer science, with an accuracy of 0.9372, an F1-score of 0.9368, and a precision of 0.9376. Physics and mathematics also demonstrate strong performance (F1-scores of 0.9115 and 0.8970), while more heterogeneous domains such as biology and finance show lower scores (F1-scores of 0.7946 and 0.7872), reflecting the complexity and variability of their terminology. Across all domains, GraphiBERT-ML consistently outperformed the baseline BERT model, confirming the benefit of external knowledge integration for scientific NER. These findings highlight domain-specific challenges in entity extraction and demonstrate the potential of knowledge-augmented models to advance cross-disciplinary analysis of machine learning research.
Attention-Based Multi-View Fusion YOLO for Non-Destructive Pineapple Sweetness Assessment Vernanda, Dwi; Apandi, Tri Herdiawan; Suhailla Binti Jili, Aisyah; Triastuti, Desy; Muhammad Fauzi, Willy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7382

Abstract

Classifying the sweetness level of pineapples is an important part of quality control, but existing methods still face issues of subjectivity and require destructive testing. Manual assessment is often inconsistent, while refractometer measurements require cutting the fruit open. Single-view computer vision offers a non-destructive alternative, yet its performance remains limited because visual cues related to sweetness appear on different sides of the fruit. This study introduces Multi-View Fusion YOLO (MVF-YOLO), a model that combines five viewpoints (full, front, left, right, and back) through an attention mechanism to perform adaptive sweetness classification. The dataset consists of 570 pineapples with TSS/TA ratios as the ground truth, producing 2,850 images grouped into three categories: sour (TSS/TA 10–20), ideal (TSS/TA 20–30), and very sweet (TSS/TA >30). MVF-YOLO achieved an mAP@0.5 of 82.1% and an overall accuracy of 84.2%, outperforming the single-view baseline by 14.3%. Attention weight analysis indicates that the full view contributes the most (0.267). With an inference time of 45.8 ms per fruit, the model is sufficiently efficient for use by farmers, distributors, and consumers. The results demonstrate that a multiview approach enhanced with learned attention can significantly improve sweetness classification accuracy without compromising computational efficiency.
Performance Comparison of VGG16 and VGG19 Architectures for Corn Leaf Disease Classification Dwi Rezeki, Nofitasari; Hanni Pradana, Zein; Panji Kusuma Praja, Muhammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.5956

Abstract

Corn (Zea Mays L.) faces challenges from leaf diseases, which become severe when farmers lack the expertise to recognize and manage them. This study presents a comparative analysis of VGG16 and VGG19 architectures for detecting corn leaf diseases, highlighting their performance under standardized conditions using transfer learning. The novelty of this study lies in the direct benchmarking of both models across multiple image resolutions and training epochs, which has not been comprehensively explored in previous studies. The system categorizes diseases based on images, thereby helping farmers manage corn leaf diseases more effectively. The VGG16 architecture was chosen for its balance of depth and computational efficiency, while VGG19 offers higher accuracy due to its increased layer depth and complexity. This system is expected to assist farmers in detecting corn leaf diseases more efficiently and accurately than previously possible. The dataset used in this study consists of 4198 images, divided into four categories: Healthy, Blight, Common Rust, and Gray Leaf Spot. The dataset was split into 80% for training and 20% for testing purposes. The classification results using 2 architectures, VGG16 and VGG19, with the use of the SGD optimiser, show that VGG19 outperforms VGG16. The VGG19 model demonstrated a performance level of 92.74% accuracy, alongside 91% for precision, recall, and F1-score. In comparison, VGG16 achieved a slightly lower accuracy of 92.62%, with precision at 91%, recall at 89%, and an F1-score of 90%. This performance variance is attributed to the architectural depth, as VGG19 utilizes 19 layers while VGG16 is limited to 16. Ultimately, this tool aims to provide farmers with a more precise and streamlined method for identifying corn foliage conditions.
Hybrid Deep Learning Models for Free-Living Imbalanced Human Activity Recognition: Comparative Study Prathama, Aditya Heru; Joy Milliaan; Ghandy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6936

Abstract

This study presents a comparative evaluation of three hybrid deep learning models for human activity recognition (HAR) in free-living and highly imbalanced conditions: 1DCNN-ResBLSTM-Attention (Model A), Attention-Mechanism-Based Deep Learning Feature Combination (Model B), and Time-Reversal-1DCNN-ResLSTM-Attention (Model C). Each architecture integrates convolutional layers for feature extraction, recurrent networks for temporal modeling, and attention mechanisms to enhance relevant representations. The HARTH v2.0 dataset, comprising 31 subjects and 15 activity classes under strong class imbalance, is used for evaluation. Results show that soft labeling consistently improves performance by better capturing transitional uncertainty in windowed sensor data. Model A achieves the highest accuracy (96.21%) and macro-averaged F1-score (88.17%), followed by Model C with comparable performance at lower computational cost, while Model B underperforms on minority classes due to limitations of spectrogram-based representations. Across all models, persistent confusion is observed among activities with similar motion patterns, such as walking, standing, and shuffling, indicating intrinsic ambiguity in sensor signals. This study provides a controlled and standardized comparison of hybrid architectures under realistic conditions, revealing both performance trade-offs and shared limitations. The findings highlight the importance of modeling uncertainty and temporal context for improving robustness, particularly transitional and underrepresented activities.
YOLOv8 to YOLO11 Performance Benchmark and Comprehensive Architectural Comparative Review Hidayatullah, Priyanto; Syakrani, Nurjannah; Sholahuddin, Muhammad Rizqi; Gelar, Trisna; Tubagus, Refdinal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6598

Abstract

In the domain of deep learning-driven computer vision, YOLO is revolutionary. However, not all YOLO models are accompanied by academic articles and architectural diagrams. It complicates the comprehension of the model's operation. Moreover, the existing review papers fail to examine each model comprehensively. This work aims to provide a thorough comparative analysis of the architectures from YOLOv8 to YOLO11, allowing readers to swiftly understand the operational mechanisms and differences among the models. We analyzed the architecture of each YOLO version by reviewing relevant scholarly articles, official documentation, and examining the source code. In particular, we discovered that YOLOv8 through YOLO11 differ in novelty while sharing similarities in the anchor-free and Non-Maximum Suppression (NMS) aspects, except YOLOv10 (NMS-free). Each also has drawbacks, such as differing levels of complexity in the way features are connected (v8), architectural structure and training (v9), training methods or dual assignments (v10), inference, and code implementation (v11). While each version improves architecture, some blocks remain unchanged. This study helps readers understand different YOLO version architectures and inspires how to improve their performance. It also provides readers with a comprehensive architecture diagram and detailed descriptions of each block, serving as a reference for both academic and practical applications. In terms of performance, a benchmark using the Roboflow 100 dataset reveals that YOLOv9 achieves superior accuracy; however, it is eight times slower owing to its NMS mechanism. YOLOv10 is the fastest but least accurate, whereas YOLOv8 and YOLO11 provide a balanced compromise between speed and accuracy.
Log-Scale Correlation Classifier for Mushroom Identification in Agricultural Internet of Things Systems Ordiyasa, I Wayan; Diqi, Mohammad; Hiswati, Marselina Endah; Rahmayanti, Dian Rhesa; Basuki, Umar; Hafizah, Ida
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6841

Abstract

Classifying edible and poisonous mushrooms is crucial to food safety, as misidentification can pose severe toxicological risks. Conventional probabilistic classifiers, such as Naïve Bayes and Logistic Regression, often underperform on categorical datasets with correlated attributes and skewed distributions. This study introduces the Log-Scale Feature Correlation Classifier, a novel probabilistic framework that integrates logarithmic transformation and correlation-weighted probability estimation to address these challenges. Using the UCI Mushroom dataset and a 10-fold cross-validation scheme, LSFCC was benchmarked against standard models. The results demonstrate that LSFCC achieved consistently superior accuracy (0.99), precision, and recall, significantly outperforming both Logistic Regression and Naïve Bayes, as confirmed by statistical tests (p<0.01). Its lightweight design and interpretability make it highly suitable for real-time deployment on resource-constrained IoT devices, particularly within Agricultural IoT systems for autonomous mushroom identification. Future research will explore LSFCC’s adaptability to noisy, multimodal data and hybrid architectures, ensuring broader applicability in real-world bioinformatics and food safety domains.
Deployment-Optimized Deep Learning Model for Coconut Shell Quality Identification in Charcoal Production Andi Anzanul Zikra; Rahmat Yanuary; Komala Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7000

Abstract

Coconut shells are a vital raw material for charcoal briquette production, and ensuring their quality is crucial for producing high-quality charcoal products. This study develops an optimized deep learning model for detecting, classifying, and evaluating coconut shell quality using Mask R-CNN, with a specific focus on bridging the gap between theoretical model development and practical industrial implementation. Unlike previous studies that only evaluate model performance in Integrated Development Environments (IDEs), this research conducts comprehensive evaluation across both IDE and industrial web platforms. The Mask R-CNN model is modified by replacing the default ResNet-101 backbone with optimized variants including ResNet50-FPN and MobileNet-FPN to address performance degradation during deployment. A dataset comprising 1,611 coconut shell images with 8,922 annotated objects across four quality classes was created for training. Experimental results demonstrate that MobileNet-FPN achieves optimal balance between accuracy and computational efficiency, with 96% mAP@0.50 on training and 84% mAP@0.50 on validation. Class-wise analysis reveals "Wet and Fibrous" as the most challenging class (74.8% AP@0.50) due to feature overlap with parent classes and class imbalance effect, while "Clean and Dry" achieves highest performance (91.2% AP@0.50) due to distinctive visual characteristics. Statistical analysis confirms significant performance differences between architectures (p < 0.05). Deployment evaluation reveals MobileNet-FPN consistently achieves average inference time of 4.79 seconds on web platform with lowest variance (SD = ±2.41s), suitable for industrial quality control applications. The developed Flask-based web application enables charcoal briquette producers to evaluate raw material quality efficiently, demonstrating the importance of deployment-aware optimization for practical industrial application.

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