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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 1,100 Documents
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Flood Risk Mapping in Batu Pahat Using GIS and Analytic Hierarchy Process Zainudin, Muhammad Ammar Asry; Abddullah, Mohd Asrul Affendi; Abdullah, Nazirah Mohamad; Che Him, Norziha; Sufahani, Suliadi Firdaus
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.v10i2.6644

Abstract

Flooding represents an ongoing natural disaster which creates major hazards that endanger human life and destroy buildings and vital systems. This research project develops a flood risk map for Batu Pahat Malaysia by combining Geographic Information Systems (GIS) with Python-based Analytic Hierarchy Process (AHP) technology. The flood-prone area identification process needs to evaluate high-risk zones and study spatial analysis methods which predict flood risks. Researchers studied three key elements which included land cover and slope and Digital Elevation Model (DEM) based elevation data to determine their impact on flood vulnerability. The AHP process became more efficient and reproducible through Python automation which executed the AHP process for the analysis. The AHP results showed that elevation contributes 63% to flood risk assessment while slope and land cover account for 26% and 11% respectively. The flood risk map divided the area into three danger levels which included low danger areas and medium danger areas plus high danger areas that mostly existed in low-lying urban areas with gentle slopes. The predictions proved accurate because researchers validated them by comparing against actual flood data from previous events. The research demonstrates how AHP combined with GIS and Python creates an efficient flood risk assessment tool which helps with disaster planning and resource management. Future research could enhance the model by incorporating additional factors such as rainfall patterns, drainage infrastructure, and soil characteristics, further improving the accuracy of flood risk predictions.
Comparative Evaluation of Thresholding Methods for Optimized Digital Document Parsing Accuracy Muhammad Noko Darpito; Kartika Firdausy; Abdul Fadlil
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.v10i2.6982

Abstract

Automated parsing of semi-structured documents has become increasingly important, particularly in standardized formats like SATS-LN, which contain fixed-layout fields such as permit number, addresses, validity period, and item types. This study investigates the impact of two thresholding methods Otsu and Sauvola on object detection accuracy using Faster R-CNN with Detectron2. A dataset of 200 SATS-LN documents, captured via scanner and camera, was augmented into 3,600 images and labeled for seven key fields. Image quality was evaluated using PSNR, SSIM, and MSE, while detection performance was measured through mAP, AP50, AP75, AR@100, precision, recall, and F1-score. Results showed that Sauvola preserved structural layout more effectively (SSIM: 0.76 for scanner, 0.47 for camera), although Otsu achieved higher PSNR on scanned images. Sauvola attained the highest macro and weighted F1-score (0.998), with near-perfect label detection and consistent performance across augmentations. Overall, Sauvola is more reliable for enhancing segmentation and detection in layout-based document processing.
Hybrid Deep Reinforcement Learning and Particle Swarm Optimization for Accelerated Multipath Routing in Congested SDN Environments Efendi, Rissal
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.v10i2.7324

Abstract

Network congestion remains a critical challenge in dynamic communication environments, often degrading data delivery performance and Quality of Service (QoS). This study proposes a Hybrid Deep Reinforcement Learning with Particle Swarm Optimization (DRL–PSO) model to adaptively optimize routing paths and mitigate congestion in Software Defined Networking (SDN). The proposed approach integrates the exploration capability of Deep Reinforcement Learning with the fast convergence characteristics of Particle Swarm Optimization to select optimal routes based on real-time network conditions. Simulations were conducted using realistic network topologies under three traffic scenarios: normal, moderate, and congested conditions. The proposed model was compared with several baseline methods, including Pure DRL, DDPG, PPO, DQN, Multi-Agent DRL (MARL), PSO-only routing, Shortest Path First (SPF), and Equal Cost Multi-Path (ECMP). The results show that Hybrid DRL–PSO achieves the lowest latency values of 15.2 ms, 34.8 ms, and 55.3 ms, as well as the highest throughput values of 9.45 Mbps, 6.34 Mbps, and 4.27 Mbps across the three scenarios. In addition, the model maintains low packet loss rates of 0.05%, 1.2%, and 8.5%, and jitter values of 4.3 ms, 9.2 ms, and 16.6 ms, respectively. The main novelty of this work lies in integrating PSO as a pre-selection mechanism to generate K-best candidate paths, reducing the DRL action space and accelerating learning convergence for QoS-aware multipath routing. This hybrid approach also demonstrates the practical potential of combining learning-based intelligence and optimization techniques for adaptive traffic management in real-world SDN infrastructures.
Detecting AI-Generated and Authentic Artworks Using a ResNet50 Convolutional Neural Network Architecture Putri Nabila, Nadia; Suharto, Bambang; Noor Febriana, Fitri
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.v10i2.7362

Abstract

The rapid advancement of generative artificial intelligence (AI) has obscured the distinction between human- and machine-created art, posing significant challenges to authentication, copyright, and artistic integrity. This study addresses the critical need for reliable verification tools by developing and evaluating a deep learning model to automatically classify artworks based on their origin. A ResNet50 Convolutional Neural Network architecture was fine-tuned for the binary classification task. The model was trained on a custom, perfectly balanced dataset comprising 868 images (434 AI-generated, 434 authentic artworks). The training protocol included extensive data augmentation to enhance generalization and an early stopping mechanism to prevent overfitting. The experimental results demonstrate a high level of classification performance. The model achieved a validation accuracy of 86.21%, with a precision of 0.88 and a recall of 0.84 for the AI-Generated class. A Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.929, indicating robust discriminative capability. Qualitative error analysis revealed that the model's primary challenges lie in classifying hyper-realistic AI-generated images and authentic artworks with surreal or digitally abstract styles. This study validates the effectiveness of the ResNet50 architecture as a reliable and accessible tool for digital art authentication. It contributes a well-documented performance baseline on a balanced, custom dataset, providing a practical foundation for future research. This work highlights key challenges and suggests future directions, such as the exploration of more advanced architectures and the development of larger, more diverse datasets to further improve detection accuracy.
A Hybrid Intersection Filtering and Recursive Feature Elimination Technique for Efficient Feature Reduction in High Dimensional Datasets Dahlan, Akhmad; Pristyanto, Yoga; Nugraha, Anggit Ferdita; Aziza, Rifda Faticha Alfa; Purwanto, Ibnu Hadi
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.v10i2.7396

Abstract

High-dimensional datasets are commonly encountered in real-world machine learning applications and often degrade classification performance due to redundant and irrelevant features. In addition, the presence of excessive features increases computational complexity and processing time. Feature selection is therefore a crucial preprocessing step to improve model accuracy and efficiency. This study proposes a hybrid feature selection approach called Intersection Filtering based on Recursive Feature Elimination with Cross-Validation (IF-RFECV), which integrates wrapper-based and filter-based strategies to obtain a stable and optimal subset of features. The proposed method first applies Recursive Feature Elimination with Cross-Validation (RFECV) using multiple classification models to rank and select relevant features. Subsequently, an intersection filtering mechanism is employed to identify features that are consistently selected across different RFECV-based models, thereby reducing model-dependent bias and improving feature robustness. The effectiveness of IF-RFECV is evaluated using four benchmark datasets with varying dimensionality obtained from the KEEL and UCI repositories. Several classification algorithms, including Gradient Boosting, K-Nearest Neighbor, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine, are used to assess model performance. Experimental results demonstrate that IF-RFECV produces a more compact feature subset compared to conventional RFECV while achieving superior performance in terms of accuracy, precision, recall, and F1-score on most datasets, particularly those with higher dimensionality. Although IF-RFECV requires slightly higher computational time due to its two-stage process, the performance gains and improved generalization justify this trade-off. These findings indicate that IF-RFECV is an effective and robust feature selection technique for high-dimensional classification problems.
Refining CNN-Based Models for Multi-Class Corn Leaf Disease Classification Wanto, Anjar; Poningsih, Poningsih; GS, Achmad Daengs; Andini, Silfia
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.v10i2.7400

Abstract

Corn leaf disease significantly impacts agricultural productivity and national food security, particularly in regions with high dependence on maize as a staple commodity. Manual disease identification remains challenging due to the need for expert agronomists, inconsistent environmental conditions, and visual similarities among disease patterns, often resulting in delayed decision-making and inaccurate control measures. Deep learning-based image classification has emerged as an effective solution for plant disease identification; however, existing models often face limitations regarding overfitting, poor generalization, and insufficient performance when applied to multi-class agricultural image datasets. Therefore, this research aims to develop an Improved EfficientNetB0 model for the multi-class classification of maize leaf diseases comprising Healthy, Leaf Blight, Leaf Rust, and Leaf Spot categories. A dataset of 4,000 images was used and processed through resizing, normalization, and augmentation techniques. Five CNN backbones; EfficientNetB0, MobileNetV2, ResNet50, DenseNet121, and InceptionV3—were initially evaluated, and EfficientNetB0 demonstrated the highest baseline performance. The model was subsequently enhanced through fine-tuning, regularization (dropout and batch normalization), and cosine learning rate scheduling. Experimental results show that the Improved EfficientNetB0 achieved superior performance with an accuracy of 0.9671, macro precision of 0.9665, macro recall of 0.9666, and macro F1-score of 0.9661, exceeding all baseline models. These findings demonstrate that the proposed framework effectively improves maize disease classification accuracy and contributes a robust solution for smart agriculture applications. Future work may integrate real-time deployment and mobile-based decision support for field-level monitoring.
An Adaptive Feature-Aware Hybrid Resampling Strategy for Imbalanced Diabetes Classification with Integrated Balanced Index Evaluation Jasmir, Jasmir; Pahlevi, Riza; Gunardi, Gunardi; Rohaini, Eni; Annisa, Tiko Nur
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.v10i2.7418

Abstract

Class imbalance remains a critical challenge in medical data classification, particularly in diabetes prediction, as it significantly degrades minority-class sensitivity. This study proposes an Adaptive Feature-Aware Hybrid Resampling Strategy (AHRS) that dynamically integrates oversampling and undersampling based on Imbalance Ratio (IR) and Feature Importance (FI). Unlike conventional static resampling methods, AHRS iteratively adjusts class distribution while preserving informative feature structures. In addition, this study introduces the Integrated Balanced Index (IBI), a bounded composite metric integrating precision, recall, and specificity to provide a fairer evaluation of classification performance on imbalanced medical datasets. The proposed approach was evaluated using the Pima Indian Diabetes Dataset (768 instances) with K-Nearest Neighbor, Naïve Bayes, and Random Forest classifiers under 5-fold stratified cross-validation. Experimental results demonstrate that AHRS consistently outperforms SMOTE, Random Oversampling, and Tomek Links, achieving accuracy improvements of 5–7% and recall gains of up to 10%. Random Forest combined with AHRS achieved the highest IBI score of 0.90, indicating strong balance between sensitivity and specificity. The findings suggest that adaptive, feature-aware resampling combined with balanced evaluation metrics provides a reliable and interpretable framework for fair medical classification systems and Clinical Decision Support Systems (CDSS).
Comparative Analysis of YOLOv8 Segmentation Variants for Indonesian Sign Language (SIBI) Recognition Azizah, Desi Fatkhi; Handayani , Anik Nur; Wibawa, Aji Prasetya; Fukuda, Osamu
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.v10i2.7500

Abstract

The Indonesian Sign Language System (SIBI) is the officially recognized communication medium for deaf communities in Indonesia, yet its limited public use continues to create barriers in education, healthcare, and public services. Automatic sign language recognition powered by artificial intelligence provides a promising pathway to reduce these inequities. This study presents a comprehensive comparative evaluation of YOLOv8 segmentation variants for SIBI recognition, aiming to identify models that stabilize accuracy and efficiency for real-time deployment. A mono-background dataset of SIBI alphabet gestures was annotated using instance segmentation, and five YOLOv8-seg models (n, s, m, l, x) were trained and tested across multiple data-split scenarios. Performance was assessed through precision, recall, F1-score, mAP50, mAP50–95, and inference time. Results show that YOLOv8m-seg consistently achieved the best trade-off (F1-score 0.972; mAP50 0.982), while YOLOv8n-seg delivered the fastest inference speed (5.163 ms), making it suitable for resource-constrained devices. Visualization further demonstrated the models’ ability to capture hand contours and distinguish gestures accurately. Beyond technical benchmarking, this research highlights the potential of YOLOv8-based SIBI recognition as an inclusive assistive technology for bridging communication gaps in schools and clinics where interpreters are often unavailable. It also identifies governance challenges, including privacy protection, misclassification risks, and equitable access, which must be addressed for actual adoption. The findings, therefore, provide not only a contribution to computer vision research but also practical guidance for policymakers and service providers, positioning SIBI recognition systems as socially embedded technologies aligned with the goals of disability inclusion and sustainable development.
On-Device Hybrid Access Control for Personal Data Sharing using Sensitivity and Trust Parameters Geocey Shejy; Pallavi Mangrulkar
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.v10i2.7522

Abstract

Protection of Personally Identifiable Information (PII) is challenging for organisations. Access to sensitive resources, ie, databases and files, can be controlled and restricted by the Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC) and other access control models defined at the organisational level. This work proposes a lightweight on-device PII access control model based on the principle of preserving privacy at the source of data generation. The set of data shared by the user is converted into objects, and the object is treated as a resource to which RBAC, ABAC and Hybrid access controls are applied. Coarse-level access to a set of PII information, in the form of PII objects, on a user device is implemented using RBAC, while fine-grained access is defined and performed using ABAC. Hybrid access, which supports object and attribute-level access control using sensitivity, trust, and Time to Live (TTL) environment, was evaluated. The RBAC, ABAC, and Hybrid models are lightweight, highly reliable, scalable, and efficient to implement on user devices. Three models were tested for scalability up to 1000 objects and a corresponding number of attributes. This model mitigates the risk of PII exposure posed by the data-collecting organisation and enhances user consent for PII sharing.
Comparative Performance of Fuzzy Logic and PID Steering Control for Improved Swerve Autonomous Vehicles Suprapto, Bhakti; Dwijayanti, Suci
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.v10i2.7594

Abstract

This study presents a performance comparison of a fuzzy logic controller with a proportional-integral-derivative (PID) controller in an autonomous vehicle steering controller based on an improved swerve drive. The advantage of this swerve drive system is that it provides high maneuverability in tight spaces by utilizing nonlinear kinematic behavior and strong coupling between translational and rotational motions. This is a challenge for conventional control strategies. To overcome this problem, a fuzzy logic controller is used, which has the ability to work in more dynamic conditions. To support the control system for precision, a good structural design is required. The feasibility of the proposed improved swerve drive mechanical design is verified through finite element-based structural analysis to ensure that the control performance is not limited by mechanical constraints. Testing results show that the configuration of seven membership functions in the fuzzy logic controller provides the best performance, with an overshoot value of 7.33% and a steady-state error of 0.0324. Real-time testing of this electric vehicle prototype was conducted in five scenarios: straight road, 90° turn, parallel parking, obstacle avoidance, and on-the-spot maneuvering. The testing results also show that the fuzzy logic controller consistently outperforms the PID controller by reducing tracking error, minimizing overshoot, and achieving faster settling times, especially under complex motion conditions. Structural validation also confirms that this improved swerve drive, operating within the elastic limits of the material, supports the implementation of reliable control strategies.

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