Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Articles
1,048 Documents
<![CDATA[Efficient ECG-Based Sleep Apnea Detection Using CNN-GRU and Sparse Autoencoder ]]>
<![CDATA[Putra, Ramadhian Eka]]>;
<![CDATA[Isa, Sani Muhamad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5182
<![CDATA[Sleep apnea is a serious and common breathing disorder that occurs during sleep, characterized by repeated pauses in breathing that can increase the risk of hypertension, heart disease, and stroke. Early detection of sleep apnea is crucial, but conventional methods, such as polysomnography, are expensive, complex, and inefficient for mass screening. Therefore, an automated system based on physiological signals such as an electrocardiogram (ECG) is needed for a more practical and efficient approach. This study proposes a sleep apnea classification model utilizing a combination of 1D Convolutional Sparse Autoencoder (1DCSAE), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) architectures, referred to as the SAE-DEEP model. This method is designed to automatically extract features while minimizing the need for preprocessing. Four testing scenarios were conducted to evaluate the impact of signal reconstruction and preprocessing on classification performance. Experimental results show that the CNN-GRU model with signal reconstruction using 1DCSAE achieves an accuracy of 89.8%, a sensitivity of 90.1%, and a specificity of 89.2%, demonstrating balanced and stable classification performance. Additionally, this model was proven to work effectively without complex preprocessing steps, making it a potential solution for efficient sleep apnea detection systems. These findings could contribute to the development of more straightforward, reliable, and clinically viable ECG-based classification systems, as well as wearable devices. In doing so, the proposed model addresses a critical gap in sleep apnea screening, underscoring the urgent need for accessible and cost-effective diagnostic tools. ]]>
<![CDATA[Deep Learning-Based Detection of Potato Leaf Diseases Using ResNet-50 with Mobile Application Deployment ]]>
<![CDATA[Budy Santoso, Cahyono]]>;
<![CDATA[Effendi, Rufman Iman Akbar]]>;
<![CDATA[Siregar, Johannes Hamonangan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5186
<![CDATA[Plant diseases significantly reduce agricultural productivity, especially in developing regions with limited access to early detection tools. This research presents a deep learning-based approach for detecting potato leaf diseases, focusing on Early blight, Late blight, and healthy conditions. A modified ResNet-50 architecture was employed and trained using a publicly available potato leaf image dataset. Preprocessing steps included data augmentation and normalization to enhance model generalization. The model achieved a high accuracy of 99.31%, with precision, recall, and F1-score all exceeding 99%, indicating excellent classification performance. This study introduces a novel approach that improves classification performance through an optimized deep learning architecture, achieving higher accuracy compared to existing models. In addition to enhancing predictive capability, the study also addresses the practical need for accessibility by integrating the trained model into an Android-based mobile application. The application allows users to upload or capture leaf images and receive real-time predictions. The interface was designed for simplicity and usability in field conditions, making it accessible to farmers and agricultural workers. The findings demonstrate that combining deep learning with mobile technology can offer an effective and scalable solution for early disease detection in agriculture. Future work may explore cross-crop adaptability and lightweight model optimization for real-time performance on low-resource devices.]]>
<![CDATA[Alphabet Gesture Classification of Indonesian Sign Language Using Convolutional Neural Networks ]]>
<![CDATA[Gideon Simalango, Yanuar]]>;
<![CDATA[Septiarini, Anindita]]>;
<![CDATA[Wati, Masna]]>;
<![CDATA[Hamdani, Hamdani]]>;
<![CDATA[Rajiansyah, Rajiansyah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5240
<![CDATA[Indonesian Sign Language (BISINDO) serves as a communication medium for deaf individuals to engage with their environment. Alphabet gestures in BISINDO play a crucial role in the formation of words and sentences. Nonetheless, the automatic recognition of BISINDO alphabet movements remains a difficulty in the advancement of accessible technology. This research intends to categorize BISINDO alphabet gestures via the Convolutional Neural Network (CNN) model. The CNN approach was used due to its proficiency in recognizing visual patterns and images. The dataset comprises BISINDO alphabet gesture photos captured from diverse perspectives and lighting conditions. The data processing procedure encompasses pre-processing phases, including picture normalization, data augmentation, and the segmentation of the dataset into training, validation, and test subsets. The constructed CNN model has multiple convolutional and pooling layers to thoroughly extract visual characteristics. The study's results indicate that the CNN model can classify BISINDO alphabet gestures with a high accuracy of 90% on the test data. This model's deployment is anticipated to aid in the creation of automatic sign language translation programs, hence enhancing communication between the deaf community and the general populace. This study demonstrates the potential of CNN models to support the development of inclusive communication technologies for the hearing impaired in Indonesia, particularly for under-researched sign languages like BISINDO.]]>
<![CDATA[Predicting Anxiety of STMIK Palangkaraya Students Using K-Means Clustering and Gaussian Naïve Bayes ]]>
<![CDATA[Widyaningsih, Maura]]>;
<![CDATA[Rosmiati, Rosmiati]]>;
<![CDATA[Prakoso, Paholo Iman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5259
<![CDATA[Academic anxiety is a common psychological problem experienced by students, especially before final exams, which impacts learning performance and mental well-being. This study aims to identify and predict students' anxiety levels using a Machine Learning approach, specifically the web framework Gradio, through a combination of the K-Means Clustering and Gaussian Naïve Bayes (GNB) methods. The research instrument used a Google Form-based questionnaire modified from the Zung Self-Rating Anxiety Scale (ZSAS) with 20 items (K1–K20) on a Likert scale (0–3). Data were obtained from 110 students of the Information Systems and Informatics Engineering Study Program at STMIK Palangkaraya. The research process consisted of five main stages: pre-processing, clustering using the K-Means algorithm, training the GNB classification model, evaluation, and prediction of new data. The clustering results categorized the data into three levels of anxiety: Low, Median, and High. The GNB model showed 95% accuracy with a balanced distribution of evaluation metrics (precision, recall, and F1 score). Comparison with other algorithms shows that while SVM achieved the highest accuracy (100%), GNB was more balanced in handling uneven class distributions and more practical for implementation in web-based systems. This prediction system has the potential to be used as an early detection tool for student anxiety, while also supporting educational institutions in designing more targeted psychological interventions. Further improvements can be made by expanding the scope of respondents, balancing the data distribution, and testing other machine learning methods to improve model generalization. The program and data are available at: https://github.com/maurawidya75/StudentAnxiety2025.]]>
<![CDATA[Artificial Intelligence in Green and Sustainable Investment: a Bibliometric and Systematic Literature Review ]]>
<![CDATA[Kamalia, Antika Zahrotul]]>;
<![CDATA[Wibowo, Arief]]>;
<![CDATA[Mahdiana, Deni]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5287
<![CDATA[Green and sustainable investment has gained increasing global attention due to the urgency of the climate crisis, social demands, and the adoption of Environmental, Social, and Governance (ESG) principles. However, research on the application of artificial intelligence (AI) in this domain remains fragmented and lacks a comprehensive mapping. This study aims to map the trends, research directions, and key findings related to AI in green and sustainable investment using a bibliometric and systematic literature review (SLR) approach. Data were retrieved from the Scopus database and screened with the PRISMA framework, resulting in 24 articles analyzed through VOSviewer and thematic synthesis. The results indicate significant developments in energy efficiency, green buildings, machine learning, and sustainability, alongside an expanding pattern of international collaboration. Nonetheless, limitations remain, including insufficient cross-sectoral integration, limited empirical studies in developing countries, and the lack of AI models that holistically incorporate risk, ESG, and SDGs indicators. The main contribution of this study lies in providing a structured literature mapping that can serve as a foundation for developing more integrative AI frameworks and expanding research contexts to optimize sustainable green investment. These findings are expected to be valuable for researchers and practitioners in advancing innovation and strengthening the AI-driven sustainable finance ecosystem.]]>
<![CDATA[Comparison of AdaBoost and Random Forest Methods in Osteoporosis Risk Prediction Based on Machine Learning ]]>
<![CDATA[Parlindungan H, Edwardo]]>;
<![CDATA[Assegaff, Setiawan]]>;
<![CDATA[Jasmir, Jasmir]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5297
<![CDATA[This study aims to determine the most effective ensemble machine learning algorithm for osteoporosis risk prediction in resource-constrained healthcare settings, specifically comparing AdaBoost and Random Forest performance on Southeast Asian population data. We implemented nested 5-fold cross-validation on a dataset of 1,958 records with 15 lifestyle and demographic attributes. Both algorithms underwent hyperparameter optimization, and performance was evaluated using accuracy, precision, recall, F1-score, and clinical utility metrics including cost-effectiveness analysis. AdaBoost achieved superior performance with 86.90% accuracy (95% CI: 84.2-89.6%) and perfect precision (1.00) compared to Random Forest's 84.69% accuracy and 0.92 precision. Statistical significance testing confirmed AdaBoost's advantage (p=0.032). Clinical implementation in three health centers demonstrated 60% reduction in unnecessary referrals. This is the first study to compare these algorithms specifically for Southeast Asian populations with clinical validation and cost-effectiveness analysis, providing a ready-to-deploy model for resource-limited healthcare settings.]]>
<![CDATA[Random Forest and LLM Synergies Framework for Autonomous DDoS Mitigation ]]>
<![CDATA[Wiratama, Romadhon]]>;
<![CDATA[Pirdhaus, Ananta]]>;
<![CDATA[Putri Bintoro, Ellys Rahma]]>;
<![CDATA[Sari, Zamah]]>;
<![CDATA[Syaifuddin, Syaifuddin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5300
<![CDATA[Modern Distributed Denial of Service (DDoS) attacks increasingly evade traditional defenses, and while Machine Learning (ML) has improved detection accuracy, a critical challenge remains in bridging detection with effective automated mitigation. This paper introduces a novel framework centered on a cognitive agent that synergistically combines high-speed ML detection with the advanced reasoning capabilities of a Large Language Model (LLM) for autonomous DDoS mitigation. The proposed architecture operates as a closed-loop security system. Following a data preprocessing phase that includes one-hot encoding and Standard Scaling (z-score normalization), a fine-tuned Random Forest model was identified as the optimal detector with 95.99% accuracy on the UNSW-NB15 dataset, which in turn triggers the LLM-based agent. This agent autonomously generates both human-readable incident explanations and machine-executable mitigation commands. Crucially, all generated commands undergo a syntax and logic validation step before execution to ensure operational integrity. Empirical results demonstrate the framework's efficacy, achieving a complete end-to-end detection-to-mitigation cycle in 24.20 seconds. This work validates that the unified approach presents a viable and transparent paradigm, contributing to the field of cybersecurity by enhancing automated mitigation and analytical processes through responsive and intelligent defense mechanisms.]]>
<![CDATA[Comparative Analysis of Machine Learning-Based Software Defect Prediction in Object-Oriented and Structured Paradigms Using Apache Camel and Redis Datasets ]]>
<![CDATA[Nasiri, Asro]]>;
<![CDATA[Setyanto, Arief]]>;
<![CDATA[Utami, Ema]]>;
<![CDATA[Kusrini, Kusrini]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5315
<![CDATA[Software Defect Prediction (SDP) is a crucial component of software engineering aimed at improving quality and testing efficiency. However, the majority of SDP research often overlooks the fundamental influence of the programming paradigm on the nature and causes of defects. This study presents a comparative analysis to identify the most influential software metrics for predicting defects across two distinct paradigms: Object-Oriented (OOP) and Structured. To ensure modern relevance and reproducibility, we constructed two new datasets from large-scale, open-source projects: Apache Camel (Java) for OOP and Redis (C) for Structured which exhibited realistic defect rates of 14.4% and 21.8%, respectively. The dataset creation process involved mining Git repositories for defect labeling and automated metric extraction using the CK and Lizard tools. Correlation analysis and baseline modeling using Random Forest revealed significant differences between the paradigms. In the OOP system, dominant defect predictors were related to the complexity of the class interface and features (e.g., uniqueWordsQty, totalMethodsQty, WMC, CBO). Conversely, defects in the structured system were strongly correlated with size and algorithmic complexity (e.g., file_tokens, file_loc, file_ccn_sum). Although the baseline models performed well (ROC–AUC = 0.82–0.87), the significant class imbalance resulted in low recall (44–50%). This motivates the need for more context aware approaches. These findings underscore that effective SDP strategies must be tailored to the underlying programming paradigm.]]>
<![CDATA[Improved Contrast and Clarity in Plant Microscopic Images using Contrast Limited Adaptive Histogram Equalization ]]>
<![CDATA[Hidayat, Eka Wahyu]]>;
<![CDATA[El Akbar, R Reza]]>;
<![CDATA[Anshary, Muhammad Adi Khairul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5333
<![CDATA[This research aims to enhance the quality of microscopic plant images which often suffer from low contrast and noise, hindering both visual and automated analysis. We propose the application of the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to address this issue. Implementation was carried out using MATLAB, processing a dataset of microscopic images from the Biology Laboratory of Siliwangi University. The research methodology includes image pre-processing, applying CLAHE with a Tile Grid Size of 8×8 and a Clip Limit of 0.02, and a quantitative evaluation using full-reference metrics such as MSE, PSNR, SSIM, RMSE, and FSIM. The results show that the application of CLAHE consistently demonstrated a significant improvement in image quality. Based on calculations, the lowest MSE value was found in the “monokotil (L.S)” image with 644.046 and the highest in the Monocotyledon Stem image with 6,298,683. The highest PSNR value was achieved by the “monokotil (L.S)” image with 46.225 dB, while the lowest was in two Monocotyledon Stem images, at 25.174 dB and 23.422 dB. The highest SSIM value was also in the “monokotil (L.S)” image with 0.946, indicating a very high structural similarity. Likewise, the highest FSIM value was also found in the “monokotil (L.S)” image with 0.979. This enhancement is crucial for botanical analysis and bioinformatics applications, as it effectively increases contrast, reduces noise, and preserves structural integrity, thereby facilitating the identification of fine details in microscopic images. These results establish a reproducible enhancement baseline that strengthens downstream botanical analytics.]]>
<![CDATA[Natural Language Processing (NLP) and Support Vector Machine (SVM) Optimization in Detecting Phishing Website URLs ]]>
<![CDATA[Aritonang, Mhd Adi Setiawan]]>;
<![CDATA[Simanulang, Maradona Jonas]]>;
<![CDATA[Batubara, Toras Pangidoan]]>;
<![CDATA[Zega, Imanuel]]>;
<![CDATA[Afrizal, M Hafis]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5334
<![CDATA[Phishing remains one of the most pervasive cyber-threats, with recent reports indicating a sharp rise in both volume and sophistication of attacks. According to the Anti‑Phishing Working Group, phishing incidents reached nearly 1 million in Q4 2024. To address this evolving threat, this study aims to develop an automated phishing-URL classification system based on Natural Language Processing (NLP) and Support Vector Machine (SVM). We utilised the Kaggle "PhiUSIIL Phishing URL Dataset" comprising 256,795 URL records and applied comprehensive preprocessing, feature extraction (structural URL features plus NLP-based keyword analysis), and SVM training with grid search optimisation. Evaluation was performed via confusion matrix and standard metrics of accuracy, precision, recall and F1-score. The best model achieved an accuracy of 99.99%, precision of 99.98%, recall of 100%, and F1-score of 99.99%. These results demonstrate that the combined NLP + SVM approach can effectively distinguish phishing from legitimate URLs with very high reliability. The proposed system contributes to cybersecurity by offering a feasible AI-based solution for real-time URL screening that can be integrated into browser extensions or enterprise email filters to bolster phishing defences.]]>
