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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 1,046 Documents
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Adaptive Stress Prediction with GSR, SMOTE Balancing, and Random Forest Models Surakusumah, Rino Ferdian; Apza, Rechi Yudha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6588

Abstract

Stress is a pervasive condition that affects mental health, productivity, and quality of life across populations. Traditional methods for stress assessment, such as the Perceived Stress Scale (PSS), rely on retrospective self-reporting and are limited by subjectivity and delayed feedback. To address this gap, this study developed an integrated real-time stress monitoring system combining Galvanic Skin Response (GSR) sensors, Internet of Things (IoT) technology, and machine learning algorithms. Primary GSR data were collected from 30 participants under varied conditions, supplemented by secondary data from the WESAD dataset. A Random Forest classifier was employed to categorize stress into four levels: normal, mild, moderate, and severe. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, leading to improved model robustness. The system achieved a cross-validated classification accuracy of 69%, with substantial improvements in the detection of moderate and severe stress cases compared to traditional threshold-based methods. A strong agreement (Cohen’s Kappa κ = 0.82) was observed between system predictions and PSS-based stress assessments. Feature importance analysis identified mean GSR value and Skin Conductance Response (SCR) amplitude as the most influential indicators of stress. The system was evaluated for usability, receiving high user ratings in terms of accessibility, simplicity, and interactivity. A simple Python-based command-line interface (CLI) was also developed for real-time stress prediction based on input features. This research demonstrates the feasibility and effectiveness of combining physiological sensing, predictive analytics, and user-friendly interfaces to enable scalable and adaptive stress monitoring. Future developments will focus on integrating additional physiological modalities and deep learning techniques to enhance predictive performance and personalization in clinical and everyday contexts.
Sonified Cryptography: Secure Text Encoding with DNA and Non-Speech Audio Chandrasekaran Saravanakumar; Neelamegam Subhashini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6591

Abstract

The increasing demand for data security in digital communication, particularly in high-risk sectors like defense, has led to the exploration of innovative encryption approaches. This study presents a dual-layer encryption model that enhances information concealment by integrating DNA-based cryptography with audio signal encoding. Initially, plaintext is converted into binary and obfuscated using XOR operations with randomly generated DNA sequences. The resulting DNA nucleotide sequences (A, G, C, T) form the first layer of encryption. In the second layer, these sequences are audified by mapping each nucleotide to a specific frequency, thereby transforming the encrypted data into non-speech audio signals. To evaluate the integrity and uniqueness of the encryption-decryption process, Fast Fourier Transform (FFT)-based cross-correlation is applied, comparing the original and recovered audio signals. The proposed method is implemented in MATLAB and tested on various input strings. Results demonstrate significant improvements in encryption speed and security, with the added benefit of imperceptibility in audio form. The method outperforms existing DNA-based techniques in terms of computational efficiency and resistance to brute-force attacks. This hybrid cryptographic technique offers a promising solution for secure, covert data transmission in sensitive applications.
Comparing Optimization Algorithms in ANN Models for House Price Prediction in Pekanbaru Winarso, Doni; Edo Arribe; Syahril; Aryanto; Muhardi; Shahrulniza Musa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6619

Abstract

This study evaluates the performance of five optimization algorithms in Artificial Neural Network (ANN) models for predicting house prices in Pekanbaru. The optimizers tested include Adam, AdaDelta, Stochastic Gradient Descent (SGD), Nadam, and Adaptive Sharpness-Aware Minimization (ASAM). A total of 3,149 house sales records were collected from rumah123.com between January and December 2024. After cleaning 148 incomplete entries, 3,001 valid records remained. The dataset included seven features: price, location, number of bedrooms, number of bathrooms, land area, building area, and garage capacity, with the location encoded using one-hot encoding. The research involved a literature review, problem formulation, data acquisition, preprocessing, model development, and evaluation. Model performance was assessed using the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). The results show that SGD consistently achieved the best performance, particularly at a 90:10 train-test split, with the lowest MAPE (1.74%) and MSE (0.3279). Adam and Nadam also performed well, while ASAM had the highest error (MAPE 6.14%). These findings indicate that SGD was the most effective optimizer for this dataset. Future research should explore larger datasets and advanced hyperparameter tuning to improve the generalizability of this model.
Optimizing Menu Planning for Children with Autism Using Improved Multi-Goal Programming Model Mohd Rashid, Nur Rasyida; Sufahani, Suliadi Firdaus; Hamzaid, Nur Hana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6649

Abstract

Menu planning for every individual is essential to obtain a balanced and healthy food intake for growth and development. Children with Autism Spectrum Disorders (ASD) face more feeding difficulties than their peers due to neurodevelopmental disorders such as social skills problems and repetitive behaviors. They also tended to refuse or be selective for certain food intakes. Proper menu planning for children with ASD is important to maintain their overall well-being and mitigate autism-related dietary issues. The manual menu planning for children with ASD does not consider proper nutritional intake, food variation, or total cost minimization. Currently, the application of mathematical modelling for menu planning in children with ASD is limited. Thus, this study aims to explore the extent to which the optimization approach can solve the menu planning problem with more than one objective. Finally, this research constructed daily menu planning for children with ASD, which indicates the feasibility of utilizing the Improved GP (IGP) model compared to the Goal Programming model (GP) in terms of the value for the deviational variables for the unachieved goals. The unachieved deviational variables by IGP model for Day-2 had decreased by 17.69% and by 34.43 % on Day-3. The total cost of the IGP model is also less than RM 0.50 of the GP model.
Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Lestari, Tri Putri; Efrizoni, Lusiana; Handayani, Nadya Satya; Andhika, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.
Benchmarking YOLOv8 Variants with Transfer Learning for Real-Time Detection and Classification of Road Cracks and Potholes Kurniadi, Dede; Latif, A. Abdul; Mulyani, Asri; Aulawi, Hilmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6710

Abstract

Road damage, including potholes and cracks, is a significant issue frequently encountered in road infrastructure in many regions. Such conditions accelerate road degradation, increase the risk of traffic accidents, and significantly increase the maintenance and repair costs. Although several deep learning models have been proposed for road damage detection, few studies have systematically compared the performance of lightweight YOLOv8 variants using a consistent dataset. To address this gap, this study proposes a road defect detection and classification model based on the YOLOv8 series, which is enhanced using transfer learning to improve performance and efficiency. The dataset, obtained from Roboflow, comprises 3,846 images categorized into training, validation, and testing sets. Three YOLOv8 variants—YOLOv8n, YOLOv8s, and YOLOv8m—were benchmarked for performance. A performance evaluation was performed using the metrics of precision, recall, and mean Average Precision (mAP). Results show that YOLOv8m achieved the highest precision (99.00%), recall (98.40%), and mAP (99.50%). In the pothole category, precision reached 98.70% and recall 99.30%; in the crack category, precision was 99.30% and recall 97.60%. The findings demonstrate that YOLOv8, particularly the YOLOv8m variant, is highly effective for real-time road damage detection and classification, offering a viable solution for intelligent transportation systems and automated infrastructure monitoring. This research has the potential to revolutionize infrastructure monitoring by enabling scalable, real-time, and cost-effective assessments of road conditions. It minimizes reliance on manual inspections, reduces human errors, and contributes to the development of intelligent transportation systems and predictive maintenance strategies.
Enhancing Agile Defect Prediction with Optimized Machine Learning and Feature Selection Faiq Dhimas Wicaksono; Daniel Siahaan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6713

Abstract

In Agile software development, efficient defect prediction is crucial because of the rapid and iterative nature of the delivery. Conventional methods that rely on source code or commit logs often fail to capture the critical contextual signals necessary for early bug detection. This study proposes a hybrid machine learning framework that leverages enriched contextual features from Jira issue tickets and combines them with optimized feature selection techniques. Various classification models, including Random Forest, XGBoost, CatBoost, SVM, and Transformer, are employed to predict defects. To further enhance model performance, metaheuristic-based feature selection methods such as the Bat Algorithm (BA) and Particle Swarm Optimization (PSO) are applied to reduce dimensionality and improve predictive relevance. Experimental results show that Random Forest with BA optimization achieves the highest performance, with an F1-score of 0.83 and an AUC-ROC of 0.86, outperforming other models. While the Transformer model does not surpass tree-based algorithms in all metrics, it shows high recall and competitive F1-scores, making it suitable for high-sensitivity applications. These findings highlight the importance of integrating optimized machine learning models and feature selection techniques to improve model robustness, reduce computational complexity, and meet the needs of Agile development. This approach supports software teams in prioritizing quality assurance tasks, reducing long-term maintenance costs, and optimizing defect management processes.
Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic Wibowo, Mars Caroline; Setyawan, Iwan; Setiawan, Adi; Sembiring, Irwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6717

Abstract

Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS 2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, including denial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows.
BERT Model Fine-tuned for Scientific Document Classification and Recommendation Antariksa, Muhammad Deagama Surya; Sugiharto, Aris; Surarso, Bayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6789

Abstract

The increasing number of academic documents requires efficient and accurate classification and recommendation systems to assist in retrieving relevant information. This system is built using the "bert-base-uncased” model from Hugging Face, which has been fine-tuned to improve the classification accuracy and relevance of document recommendations. The dataset used consists of 2.000 academic documents in the field of computer science, with features including titles, abstracts, and keywords, which were combined into a single input for the model. Document similarity is measured using cosine similarity, resulting in recommendations based on semantic proximity. Unlike traditional approaches, which rely primarily on word frequency or surface-level matching, the proposed method leverages BERT’s contextual embeddings to capture deeper semantic meanings and relationships between documents. This allows for more accurate classification and more context-aware recommendations. Evaluation results show that the best model configuration (learning rate 3e-5, batch size 32, optimizer AdamW) achieved 89.5% training accuracy and an F1-score of 0.8947, while testing yielded 91% accuracy and 90% F1-score. The recommendation system consistently produced Precision@k values above 92% for k between 5 and 30, with Recall@k reaching 1.0 as k increased. These results indicate that the system not only performs reliably in classifying complex academic texts but also effectively recommends contextually relevant documents. This integrated approach shows strong potential for enhancing academic document retrieval and supports the development of semantically aware information management systems.
Performance Comparison of YOLOv8 and DETR in White Blood Cell Detection Rakhmatsyah, Andrian; Abdurohman, Maman; Erfianto, Bayu; Prihatni, Delita
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6864

Abstract

Automated detection and classification of white blood cells (WBCs) from microscopic images play a vital role in supporting the diagnosis of hematological diseases. Accurate and robust object detection algorithms are essential for handling interclass similarities and imbalanced datasets. This study aims to evaluate and compare the performance of two modern object detection algorithms—Detection Transformer (DeTR) and YOLOv8—in performing multiclass WBC classification using public datasets from various sources with diverse visual characteristics. Five experimental scenarios were designed based on varying class distributions and data augmentation techniques, including horizontal/vertical flipping and random rotation. Both methods were trained and evaluated on the same dataset partitions, and their performances were assessed using the following standard metrics: precision, recall, and F1-score for each WBC class. The results show that YOLOv8 consistently achieved superior and more stable performance across all scenarios, with average F1-scores close to 1.00 even in augmented and imbalanced conditions. In contrast, DeTR performed competitively in balanced scenarios but showed lower consistency, particularly in classes such as Neutrophil and Monocyte. Data augmentation positively affected both models, although the gains were more prominent in YOLOv8. This study highlights the strong potential of YOLOv8 in real-time WBC classification tasks and presents DeTR as a viable yet less-optimized approach for this application. These findings contribute to the advancement of medical image-based object detection and offer valuable insights into the selection of appropriate algorithms for hematological image analysis

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