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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 1,070 Documents
 103   104   105   106   107 
Serendipitous Recommendations for Handicraft Store Discovery in Social Commerce Using a Genetic Algorithm with Adaptive Selection Kamal, Ahmad; Binti Sura, Suaini; Po Hung, Lai; Astri, Renita
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.v10i1.7077

Abstract

In social commerce, particularly among small and medium-sized handicraft enterprises (SMEs), personalized recommender systems (RS) are crucial for enhancing store and product discovery. Conventional content-based filtering (CBF) often overemphasizes accuracy, leading to over-specialization and limiting exposure to novel or diverse items, an issue in the handicraft sector where uniqueness is valued. This study proposes a serendipitous recommendation approach using a Genetic Algorithm (GA) with adaptive selection strategies, Roulette Wheel Selection (RWS), Tournament Selection (TnS), and Rank-Based Selection (RBS), to balance relevance and unexpectedness. Handicraft store attributes, such as product types, materials, and services, are encoded in a 19-bit chromosome and evaluated via a hybrid fitness function. Tested on real data from West Sumatra SMEs, the model is assessed using Precision, Recall, Novelty, and Serendipity metrics. Results show that the GA-based adaptive selection approach outperforms baseline CBF in producing more diverse and surprising recommendations, fostering exploratory shopping experiences and supporting the discovery of unique local products in social commerce ecosystems.
Detecting Language Anxiety in Indonesian Students: Deep Learning and Traditional Classification Methods for English Learning Anxiety Lhaksmana, Kemas Muslim; Falif , Muhammad Sya’bani; Nurhayati, Iis Kurnia; Rezaldi, Muhammad Yudhi; Prakasa, Esa; Roedavan, Rickman
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.v10i1.7109

Abstract

Mastery of the English language represents a fundamental determinant of professional achievement, particularly for individuals seeking to develop their careers and participate in international contexts. However, when learning a foreign language such as English, Indonesian students may experience language anxiety that cause them to hesitate to practicing English in the class, both in orally and in writing. For English teachers, it is crucial to identify students experiencing language anxiety early on, so that they may provide appropriate teaching strategies and interventions from the first class meeting. To address this issue, this study compares machine learning methods to provide a solution for early detection of students experiencing language anxiety. Furthermore, these methods are classification models, including LSTM, GRU, decision tree, naïve Bayes, logistic regression, and SVM. The implementation of each of these models is combined with different text representation techniques, such as Word2Vec, BERT, FastText, Glove, and TF-IDF. The advantage of our model is that despite the imbalance, limited, and smaller than baseline dataset size, this research finds that GRU with focal loss achieves the highest F1 score of 0.89. This result outperforms our baseline and thus suggests that this method is effective in detecting students who experience language anxiety.
Unifying Knowledge, Reasoning, and Hierarchy for Classifying Harmful Content Budiarto, Raden; I Gede Maha Putra
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.v10i1.7115

Abstract

The spread of negative, engagement-driven content online causes significant societal harm, requiring advanced automated moderation tools. However, current classification systems often treat harmful content subtypes as independent, "flat" categories, which hinders their ability to thematically overlap content. This study designed and validated a novel integrated framework to accurately and transparently classify such complex cases. We proposed KG-DToT-HTC, a hybrid framework that synergistically combines three methodologies: a predefined Hierarchical Text Classification (HTC) taxonomy to structure the decision-making process; a domain-specific Knowledge Graph (KG) to provide factual, real-world context; and Decision Tree-of-Thought (DToT) prompting to guide a Large Language Model through an explicit, step-by-step reasoning process. On a real-world dataset of harmful Indonesian news, the proposed framework achieved a state-of-the-art Macro-F1 score of 0.934, representing a nearly 15-percentage point improvement over a zero-shot baseline. Ablation studies confirmed that each component—hierarchy, knowledge, and reasoning—provided a distinct and critical contribution to the final performance. The major conclusion of this study is that a synergistic architecture is essential for the accurate classification of complex harmful content. This work demonstrates a viable path toward "glass-box," interpretable AI moderation systems whose decisions are not only highly accurate but also fully auditable.
A Hybrid Round-Robin Scheduler for GPU Batch Rendering in Constrained Cloud Environments Purwanto, Ibnu Hadi; Dhani Ariatmanto; M. Shahkhir Mozamir; Afifah Nur Aini
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.v10i1.7117

Abstract

Creating high-quality 2D and 3D assets is essential for digital content, but inefficient scheduling and inaccurate time estimates often hamper the rendering process. Traditional methods, which assume rendering time is directly proportional to frame count, fail to account for variations in scene complexity, resulting in severe estimation errors averaging 97.0% across all tasks. We propose a Hybrid Round-Robin Scheduler (HRRS) that intelligently manages batch rendering tasks through complexity-aware classification. Our method first categorizes tasks by complexity (Low, Medium, High) and routes them to appropriate queues with tiered quantum allocations. It then employs non-linear time estimation models and dynamically adjusts processing priorities based on real-time performance metrics. We evaluated our scheduler against standard algorithms—First-Come-First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR)—using 21 diverse rendering tasks with frame counts ranging from 10 to 420 frames. The results demonstrate that our approach reduces average waiting time by 45.9% (from 29.63s to 16.02s) and cuts bottleneck-induced delays by 78% (from 41s to 9s), while maintaining optimal CPU utilization at 85% and limiting context switches to only nine occurrences. A key finding reveals that complexity, rather than frame count, is the primary driver of processing time; high-complexity tasks required significantly longer processing (averaging 238.27 seconds) compared to medium-complexity tasks (averaging 34.52 seconds), representing a 6.9-fold performance differential. Our hybrid framework effectively overcomes the primary limitations of existing algorithms: it prevents bottlenecks from large tasks (FCFS), avoids the parallelism issues of SJF, and minimizes the performance overhead from frequent switching in Round Robin. This work provides a robust foundation for intelligent resource allocation in cloud rendering environments where task demands are variable and difficult to predict, establishing that effective scheduling requires complexity-aware algorithms rather than universal approaches.
Benchmarking Transformer Architectures for Chest X-ray Classification Pinem, Joshua; Astuti, Widi; Adiwijaya
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.v10i1.7132

Abstract

Lung diseases remain a major global health concern, necessitating accurate and timely diagnosis. Chest X-ray (CXR) imaging is widely used but challenging to interpret due to overlapping radiographic features and subjective variability among radiologists. Deep learning approaches, particularly Convolutional Neural Networks (CNNs), have shown promise but are limited in capturing global spatial dependencies. Vision Transformers (ViTs) overcome this limitation through self-attention, making them increasingly attractive for medical image analysis. This study systematically evaluates 13 Transformer-based architectures across three CXR datasets with distinct tasks: Pneumonia (3-class: Normal, Bacterial, Viral), COVID-QU-Ex (3-class: Normal, Non-COVID Pneumonia, COVID-19), and Tuberculosis (2-class: Normal, Tuberculosis). All models were trained under a unified setup with consistent preprocessing, augmentation, and evaluation protocols. To improve robustness, a soft voting ensemble of the top five models was also implemented. Results demonstrate that Transformer-based models provide highly competitive performance. On the Pneumonia dataset, the ensemble achieved an accuracy of 0.8743 and F1-score of 0.8615, surpassing several single models such as DeiT-Base (F1 = 0.8725). On COVID-QU-Ex, the ensemble soft voting obtained 0.9593 accuracy and 0.9582 F1-score, effectively balancing precision and recall. On Tuberculosis, ViT-B/16 and MobileViT-S achieved perfect performance (F1 = 1.0), likely influenced by dataset imbalance. These findings highlight the clinical potential of Transformer-based models, particularly when combined through ensembles, for robust and accurate CXR classification.
Plant Disease Identification Using Image Processing: A Systematic Literature Review Minarni, Minarni; Rusydi, Muhammad Ilhamdi; Darwison, Darwison; Nugroho, Hermawan; Sunaryo, Budi
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.v10i1.7171

Abstract

This article is a literature review focusing on plant disease identification using image processing techniques. This review aims to provide a comprehensive analysis of dataset sources, preprocessing methodologies, segmentation techniques, feature extraction processes, and various classification methods, along with their associated accuracies. It also discusses challenges encountered and potential future research directions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a literature search was conducted in the Scopus database to obtain primary studies. The search covered Scopus-indexed journals and proceedings published by IEEE, Elsevier, Springer, MDPI, and ACM between 2019 and 2025. The initial identification phase yielded 9,286 studies screened. Further screening was performed based on specific eligibility criteria, including relevance to the topic, year of publication, subject area, document type, and articles written in English, resulting in the selection of 82 studies for the review. The findings indicate that the most commonly used dataset is PlantVillage, followed by field data. The dominant preprocessing techniques include image enhancement and augmentation. For segmentation and feature extraction, the most frequently used methods were k-means and CNN, respectively. Sixty-one studies achieved an accuracy exceeding 90%. However, several key challenges remain: data limitations, methodological issues, and practical constraints. Future research should focus on developing more representative datasets, hybrid approaches that integrate classical and deep learning methods, and lightweight, adaptive decision support systems suitable for real-world agricultural applications. This review supports continued progress in this field by providing valuable insights for researchers developing image-based methods for identifying plant diseases.
Automated Young Children’s Pain Detection via Facial Expressions with YOLO v11 Ramdhanie, Gusgus Ghraha; Nurdina Widanti; Bambang Aditya Nurgraha; Tomy Abuzairi; Nur Agustini; Dessie Wanda
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.v10i1.7206

Abstract

This study demonstrates that pain detection in young children using a YOLO v11-based deep learning model can be performed effectively. By utilizing image data taken from video recordings of immunization and IV infusion procedures, then processed into photo frames and labeled using Roboflow, the model is able to provide good evaluation results. The dataset was divided into 70:20:10 for training, validation, and testing. Model performance evaluation uses accuracy, precision, recall, and F1-score metrics, and is visualized through a performance curve and confusion matrix. The results show that YOLO v11 has great potential as a pain detection method, with an mAP@0.5 achievement of 0.893, an accuracy of 78%, a precision of 89.3%, a recall of 97%, and an F1-score of 83%. The high recall value indicates the model's excellent ability to recognize pain expressions, making it relevant for use in clinical contexts to ensure pain symptoms are not overlooked. Overall, this performance demonstrates that YOLO v11 can be a more objective and accurate approach than manual instruments, and has the potential to be developed as a tool for healthcare professionals in pediatric pain assessment.
Analysis of Backdoor Shells in Web Servers Using Splunk and SPL-Based Machine Learning Sukmaaji, Anjik; Slamet; Anastasya Putri BR, Sophie
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.v10i1.7215

Abstract

Backdoor shell attacks pose a critical threat to web server security, allowing attackers to bypass authentication and gain persistent, unauthorized control. Conventional signature-based detection methods often fail against these attacks due to their polymorphic and obfuscation techniques. To address this, we propose an integrated detection approach leveraging Splunk as a log management platform combined with Search Processing Language (SPL)-based machine learning (ML) models. This study collected and preprocessed web server log data using SPL queries, transforming it into structured features for classification. We evaluated two supervised learning algorithms, Logistic Regression and Random Forest, on a labeled dataset comprising both normal traffic and simulated backdoor shell attacks. The evaluation showed that while Logistic Regression achieved a solid performance with 93.5% accuracy and 87.8% recall, the Random Forest model significantly outperformed it. Random Forest reached an accuracy of 97.2%, with a precision of 95.8%, recall of 94.1%, and an F1-score of 94.9%. Crucially, it also reduced the false negative rate (FNR) to 2.3% and the false positive rate (FPR) to 3.5%, making it more reliable for real-time applications. Our findings demonstrate that Random Forest, when integrated with Splunk's SPL, provides a highly robust and practical detection mechanism that effectively distinguishes malicious activities. The primary contribution of this research is an end-to-end architecture that combines scalable log management, effective feature engineering, and advanced ML detection, offering a scalable and practical solution for enterprise-level security monitoring.
A Comparative Evaluation of Federated Learning Algorithms for Privacy-Preserving Academic Prediction on Heterogeneous Data Angello Qadosy Riyadi, Michael; Mariasti Dewi, Adinda
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.v10i1.7288

Abstract

The rapid growth of educational data enables predictive analytics for academic performance, yet privacy regulations like GDPR and FERPA severely restrict centralized data sharing. Although Federated Learning (FL) has succeeded in privacy-sensitive fields such as healthcare, its application in education remains underexplored, lacking systematic comparative studies of multiple FL algorithms across diverse educational datasets—especially emphasizing recall and ROC-AUC as critical metrics for early identification of students at academic risk. This study fills this gap by evaluating five FL algorithms—Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Dynamics (FedDyn), Fair Federated Averaging (q-FedAvg), and SCAFFOLD—for privacy-preserving prediction of academic outcomes. Three public datasets were purposefully selected for their representativeness and heterogeneity: Predict Students Dropout and Academic Success (binary dropout prediction with socioeconomic factors), Student Performance (multi-class grade prediction in secondary education), and xAPI-Edu-Data (multi-class performance based on online learning activities). Local neural networks employed Stratified 5-Fold Cross-Validation, while FL algorithms ran for 50 communication rounds. Global models, particularly q-FedAvg and FedProx, consistently surpassed local models, with q-FedAvg achieving 0.7668 accuracy, 0.6813 recall, and 0.8810 ROC-AUC on Predict Students Dropout; 0.8580 accuracy and 0.9871 recall on Student Performance; and 0.7396 accuracy and 0.8815 ROC-AUC on xAPI-Edu-Data. Paired T-tests confirmed significant recall gains for most global models (p < 0.05). These results highlight FL’s ability to handle data heterogeneity and privacy constraints while improving predictive performance, thereby supporting timely educational interventions and enhanced student retention policies.
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.

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