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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 1,164 Documents
 113   114   115   116   117 
Comparative Evaluation of Deep Learning Models for Real-Time Waste Classification Sari Dewi Budiwati; Guntur Prabawa Kusuma; Nishanth Shanmugam; Sujai Samraj; Bagas Catur Santoso; Ova Syahdira Pramondari; Fakhira Nur Aini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7455

Abstract

Waste management has become a pressing global challenge due to rapid urbanization and population growth, leading to increased environmental pollution and resource depletion. Automating waste segregation using deep learning can significantly improve recycling efficiency and reduce manual labor. In this study, five deep learning models: YOLOv8, YOLOv5, MobileNetV2, Single Shot Multibox Detector (SSD), and Google’s pre-trained EfficientNet, were evaluated for real-time waste detection and classification. A dataset consisting of 15,150 images across 12 waste categories was used for training and testing, representing common household waste materials and evaluated using real-world objects such as water bottles, glass bottles, cans, and used face masks. Testing was conducted on both a high-performance workstation and a Raspberry Pi 4 edge device to assess detection accuracy, inference speed, and practical deployment feasibility. The results indicate that YOLOv8 achieved the highest average accuracy of 83% with reliable performance across diverse object types, whereas EfficientNet achieved moderate accuracy of approximately 60% with higher inference latency. Lightweight models such as MobileNetV2 and SSD were computationally efficient but exhibited lower accuracy, at 42% and 41%, respectively, particularly when handling irregular or overlapping objects. These findings demonstrate that YOLOv8 provides the most effective balance between accuracy and real-time performance, making it well suited for intelligent waste sorting systems.
Benchmarking Lightweight Machine Learning for Disaster Prediction: Accuracy, Latency, and Readiness Muhammad Amanulloh Mz; Oky Dwi Nurhayati; Jatmiko Endro Suseno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7570

Abstract

Natural disaster early warning systems are often constrained by high computational latency, which impedes the timely dissemination of critical information. This study proposes a disaster impact prediction framework designed to optimize inference speed through a lightweight machine learning approach. Utilizing a comprehensive historical dataset from the Indonesian National Board for Disaster Management (BNPB) comprising 28,773 disaster records across 34 provinces from January 2018 to May 2024, this research evaluates the performance of XGBoost and LightGBM against a Gated Recurrent Unit (GRU) model for predicting infrastructure damage. SMOTE was applied exclusively during the training phase to address class imbalance without affecting inference speed. The results demonstrate that tree-based models significantly outperform GRU in both accuracy and speed. XGBoost achieved the lowest Mean Absolute Error (MAE of 1.1649) and the fastest inference latency (0.0026 ms per sample), followed by LightGBM (MAE of 1.4006, latency of 0.0064 ms), while GRU yielded a substantially higher error (MAE of 1.4189) and latency (0.2493 ms). Statistical validation via the Wilcoxon signed-rank test confirmed the significance of these performance differences (p < 0.001). Beyond technical benchmarking, these findings support the development of Intelligent Decision Support Systems for disaster mitigation, where organizational readiness and technology acceptance are critical for successful adoption. This study discusses practical implications for deploying lightweight models on resource-constrained edge computing devices such as Raspberry Pi 4, demonstrating sub-millisecond response speed, horizontal scalability, and affordability under $100 per node.
Cross-Dataset Evaluation of Boosting Models for Hypertension Prediction Bety Wulan Sari; Dewi Ayu Murtiningsih; Donni Prabowo; Yoga Pristyanto; Ika Nur Fajri; Ike Verawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7620

Abstract

Hypertension remains a significant global risk factor for cardiovascular disease and related mortality, necessitating reliable early risk prediction models. Although boosting algorithms have demonstrated strong performance in structured medical data, limited studies have examined their consistency across heterogeneous datasets. This study aims to evaluate the cross-dataset performance and stability of three boosting models, such as XGBoost, LightGBM, and CatBoost, for hypertension prediction under multiple train–test split ratios. Two independent structured datasets were analyzed using 60:40, 70:30, 80:20, and 90:10 splits. To identify the optimal hyperparameters, grid search was performed using repeated stratified 5-fold cross-validation with three repetitions. Model effectiveness was measured using the evaluation metrics of accuracy, precision, recall, F1-score, and AUC. Results show that Dataset 1 gained consistently high predictive performance (accuracy > 0.98; AUC ≈ 1.00), indicating strong and well-separated predictive signals, whereas Dataset 2 demonstrated substantially lower discriminative ability (accuracy ≈ 0.71–0.72; AUC ≈ 0.50), suggesting limited predictive structure. Across both datasets, CatBoost consistently obtained the highest accuracy, particularly at the 90:10 split ratio. These findings demonstrate that dataset characteristics critically determine model effectiveness and that among the evaluated boosting algorithms, CatBoost delivered the strongest overall predictive performance.
Multimodal Contextual Learning for Maize Disease Diagnosis: Fusing Visual and Environmental Data Hilton Kudzai Chironga; Ary Murti Muhammad; Erwin Susanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7671

Abstract

Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) threaten food security in Zimbabwe, yet traditional image-based deep learning models lack adaptability to varying agro-environmental conditions. This study presents a multimodal contextual learning framework that fuses visual symptoms with environmental data to improve diagnostic accuracy and interpretability. A multimodal Convolutional Neural Network integrates leaf imagery with six environmental parameters (temperature, humidity, rainfall, soil moisture, leafhopper count, days after planting) using a late-fusion architecture with multi-task learning for simultaneous disease classification and five-stage severity estimation, enhanced by Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explanation. A synthesized dataset (n=9,356) representative of Zimbabwean conditions was used. The multimodal approach achieved 94.3% accuracy versus 91.5% for image-only baselines (p<0.001, 95% CI), a 33.2% relative error reduction, with MSV-MLN confusion decreasing by 40.7% (113 to 67 misclassifications). Multi-task performance reached 94.5% for classification and 81.5% for severity estimation (F1=0.805). Grad-CAM analysis revealed environmental integration enhanced attention by 38.1% and localization by 45.2%, with leafhopper count showing the strongest correlation (r=0.62). Ablation studies confirmed that environmental features provided the largest accuracy gain (+3.71%, p<0.001). This work demonstrates that integrating environmental context with visual symptoms enhances diagnostic accuracy and model interpretability, establishing a foundational proof-of-concept for context-aware agricultural AI.
Temporal Semantic Flow Networks for Analyzing Topic Evolution in Educational Data Mining Firman Edi; Ambiyar; Waskito; Samsir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7755

Abstract

This study aims to provide a comprehensive longitudinal analysis of the evolution of Educational Data Mining (EDM) research from 2014 to 2024, with a particular emphasis on learning analytics and student success prediction. We have developed and implemented the Temporal Semantic Flow Networks with Attention-based Topic Evolution (TSFN-ATE) methodology, which reconceptualizes the evolution of scientific discourse as continuous semantic flows. This framework integrates multi-scale temporal attention mechanisms and dynamic network representations to analyze Scopus-indexed keyword data from 436 EDM articles. The analysis identified significant transformations within the discourse of EDM, with a 333% increase in research intensity related to predictive modeling and an unprecedented 2,650% growth in the application of deep learning from 2014 to 2024. Deep learning attained the highest semantic flow score (0.94), signifying its successful integration across various EDM subfields, while machine learning emerged as the central methodological bridge, exhibiting the strongest network centrality (0.74). Multi-scale temporal attention analysis revealed differential patterns across time scales, with machine learning receiving the highest recent attention (0.85 at a 1-year scale), whereas learning analytics demonstrated sustained long-term influence (0.75 at a 5-year scale). Concept drift detection identified four major paradigm shifts, with the 2018-2019 deep learning integration representing the most significant conceptual disruption (semantic stability index 0.45). Collectively, these four identified paradigm shifts — from traditional machine learning through deep learning integration to the emergent ethical turn — reveal that EDM is not simply accumulating methods but undergoing continuous conceptual reorganisation; understanding and anticipating these shifts is therefore essential for researchers, institutions, and policymakers seeking to align educational technology development with evolving scientific and societal priorities.
CVI-Validated Indo-Transformer Framework for Intelligent Cooperative Supervision Syahroni Hidayat; Afriani Fajar Navissaturrisqi; Feddy Setio Pribadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7606

Abstract

Cooperative supervision reports contain complex narrative structures and overlapping administrative terminology, complicating automatic classification into governance, risk profile, financial performance, and capital adequacy. Reliable automation is particularly important for accelerating the analysis of supervisory findings while addressing limited labeled data and imbalanced categories. This study aimed to develop and externally evaluate a text-classification framework combining quantitatively validated Generative Artificial Intelligence (GenAI) labeling with conventional and Transformer-based models. Data comprised 294 preprocessed sentences collected from the Department of Cooperatives, Small and Medium Enterprises, Industry, and Trade of Semarang Regency during 2023–2025. Few-shot annotations were generated using ChatGPT, Gemini, Perplexity, and DeepSeek, and three-model combinations were evaluated using the Content Validity Index (CVI); majority voting from the best combination established ground truth. TF-IDF with Logistic Regression and Support Vector Machine served as baselines, whereas IndoBERT and IndoRoBERTa represented contextual models. Performance was assessed through stratified five-fold cross-validation and external testing on 58 unseen sentences. ChatGPT–Gemini–Perplexity achieved the highest Scale-Level CVI of 0.898. IndoBERT obtained the best cross-validated F1-score of 0.9099, exceeding IndoRoBERTa (0.8217), Logistic Regression (0.8004), and SVM (0.7863). On unseen data, IndoBERT retained an F1-score of 0.862, compared with 0.759 for IndoRoBERTa. These findings demonstrate that CVI-validated ensemble GenAI can construct consistent labels for low-resource administrative texts and that IndoBERT provides the strongest and most stable generalization for cooperative supervision classification. The framework offers a practical basis for scalable annotation and reliable automated support for evidence-based supervisory decision-making.
LightGBM for Liver Disease Detection with Hybrid Hyperparameter Optimization Fajar Ratnawati; Agus Tedyyana; Johny Custer
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7357

Abstract

In response to the growing burden of liver-related disorders, this research develops a supervised learning approach using the Light Gradient Boosting Machine (LightGBM) algorithm to support the early identification of Non-Alcoholic Fatty Liver Disease (NAFLD). The study focuses on constructing and assessing a robust classification model that differentiates individuals with NAFLD from those without the condition based on routinely collected clinical indicators and lifestyle-related characteristics. The dataset, obtained from an open-access NAFLD repository, consists of 1,700 patient records with 10 predictor variables and one binary diagnosis label. The proposed framework employs a stratified shuffle split evaluation scheme with 5-fold and 10-fold cross-validation, using out-of-fold (OOF) probabilities to compute overall performance metrics. The baseline LightGBM model already demonstrated strong performance, achieving 88.94% accuracy, 90.74% precision, 88.99% recall, 89.85% F1-score, and 92.52% AUC under 10-fold cross-validation. To further improve predictive performance, hyperparameter tuning was performed using Optuna and Bayesian Optimization. Among the evaluated approaches, Bayesian-optimized LightGBM achieved the best results, with 93.17% accuracy, 94.49% precision, 92.52% recall, 93.72% F1-score, and 93.28% AUC under 10-fold cross-validation. These findings indicate that systematic hyperparameter optimization can improve the discriminative capability of LightGBM for NAFLD detection and support its potential as a reliable decision-support tool in clinical settings.
Efficient Assured Cloud Data Deletion and Verification Scheme Using AES-XOR Encryption and Merkle Trees Huda Sharaf Eldin Shakur; Baban Ahmed Mahmood
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7421

Abstract

With the increasing interest of cloud computing users in storing and processing their data, the need for secure and permanent data deletion has grown. This research paper proposes a practical, evaluable, and testable mechanism for verifying data integrity and ensuring its deletion. The proposed system integrates sequential XOR technology with AES-based block-level encryption, thereby achieving strong interconnection between encrypted blocks. This prevents block separation, decryption, or tampering during transmission and storage. The encryption key is divided into independent parts, the purpose of which is to prevent any single party from possessing the complete encryption key. This ensures that the file cannot be recovered if any part of the key is deleted. The encrypted blocks are then arranged with one of the key parts in a Merkel tree structure to guarantee data integrity and verifiability during storage and retrieval. Through experimentation and providing similar levels of confidentiality and data recovery, the experimental results showed that this mechanism achieves competitive computing efficiency with reasonable storage and communication costs compared to basic methods. This solution was implemented on a real cloud platform within the Microsoft Azure environment, proving its feasibility, security and reliability without the need for major modifications to the existing cloud infrastructure.
Fusion-Net50-Based Soccer Player Re-Identification and Team Clustering for Speed and Movement Analysis Syahrul Idhom; Moch Arief Soeleman; Catur Supriyanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7457

Abstract

Comprehensive analysis of soccer matches requires substantial human resources as well as specialized equipment with relatively high costs. The development of Computer Vision (CV) technology, particularly the Multi-Object Tracking (MOT) approach, enables contactless and efficient analysis of player performance. However, the main challenges in MOT for soccer, namely player re-identification, tracking ID switches, as well as inter-frame identity consistency, especially under occlusion conditions and low-confidence detections, can cause the analysis data to become invalid. This research aims to improve object identity consistency (Tracking ID) in video-based soccer match analysis by integrating detection, tracking, re-identification, and team identification methods. This research proposes the integration of YOLOv8n- and ByteTrack-based tracking methods with a Re-ID embedding mechanism, low-confidence detection handling, as well as ResNet-50-based Feature–Position Fusion to maintain identity consistency (Track ID) throughout the video sequence. Furthermore, team identification is consistently performed using a Color Embedding approach with a ResNet-50 backbone and the K-Means clustering algorithm. The experimental results show that the integration of all these methods successfully maintains consistent Track ID with a total of 25 Track IDs (Players 21 Track IDs, Goalkeeper 1 Track ID, Referee 2 Track IDs, Ball 1 Track ID). This performance is supported by evaluation metrics indicating strong performance, namely HOTA of 91.9%, MOTA of 89.8%, and MOTP of 81.0%. The effectiveness of the Re-ID module in maintaining object identity consistency is reflected in the IDF1 score of 94.9%. Supported by association metrics (AssA 93.5%, AssRe 94.8%, AssPr 96.3%), these results indicate that trajectories are formed with a relatively low level of association errors, thereby supporting subsequent analyses such as speed estimation and player movement visualization in a more stable manner.
Efficiency and Comparative Performance of LBP-Based Random Forest and SVM for Toraja Buffalo Classification Abdul Rachman Manga&#039;; Anik Nur Handayani; Heru Wahyu Herwanto; Rosa Andrie Asmara; Syamsul Bahri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7536

Abstract

Toraja buffalo holds significant cultural and economic value, yet automated classification remains challenging due to subtle visual differences between types. This study evaluates the efficiency of Local Binary Pattern (LBP) combined with Random Forest (RF) and Support Vector Machine (SVM) for classifying six Toraja buffalo types: Balian, Lotong Boko, Pudu, Saleko, Todi, and Ulu. Unlike complex deep learning approaches, this research focuses on a computationally efficient framework by extracting texture features from multiple body parts: head, eyes, horns, body, and tail to capture distinctive patterns. The methodology involves multi-part feature fusion and a comparative analysis of ensemble versus kernel-based learners. Experimental results demonstrate that the LBP-Random Forest model significantly outperforms SVM, achieving a superior accuracy of 92.08% compared to 64.17%. The findings highlight that the proposed LBP-RF integration provides a robust and resource-efficient alternative for livestock image classification, balancing high diagnostic accuracy with lower computational requirements.

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