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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 7 Documents
 1 
Search results for , issue "Vol 10 No 2 (2026): April - In progress" : 7 Documents clear
A Novel Framework for Dynamic Semantic Network Analysis with Evolutionary Community Detection Applied to LMS Research Anip Febtriko; Muhammad Giatman; Irfan, Dedy; Syafrijon, Syafrijon; Tri Rahayuningsih
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7250

Abstract

The rapid proliferation of Learning Management Systems (LMS) in K–12 education has generated a substantial body of research, yet how its core themes emerge, converge, and transform over time remains insufficiently understood. Existing bibliometric and topic modeling approaches produce static snapshots of the literature, structurally incapable of capturing the dynamic epistemic processes through which research communities form and evolve. This study introduces Dynamic Semantic Network Analysis with Evolutionary Community Detection (DSNA-ECD), a novel computational framework that conceptualizes the K–12 LMS research field as a living epistemic system — a conceptual reframing that constitutes a distinct contribution to the K–12 LMS literature beyond prior static approaches. DSNA-ECD integrates three methodologically principled components: transformer-based semantic embeddings via Sentence-BERT (`all-MiniLM-L6-v2`), selected for its capacity to capture latent semantic proximity beyond lexical co-occurrence; a hybrid weighting scheme empirically calibrated to balance structural and semantic network signals; and the Leiden algorithm for community detection, preferred over Louvain for its theoretical guarantee of well-connected partitions and superior modularity optimization. Applied to a two-decade corpus of K–12 LMS publications, findings reveal a maturing field progressing from exploratory fragmentation through consolidation toward sophisticated integration of AI-enhanced adaptive systems and learning analytics. Compared to co-citation analysis, LDA topic modeling, and static semantic networks, DSNA-ECD uniquely offers semantic depth, guaranteed community coherence, calibrated hybrid weighting, and full cross-temporal trajectory tracking. Critically, findings reveal urgent underrepresentation of equity, algorithmic transparency, and ethical deployment research as AI-enhanced LMS systems proliferate, with direct implications for researchers, educational technologists, and policymakers.
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 - In progress
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 - In progress
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 - In progress
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 - In progress
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 - In progress
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 - In progress
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.

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