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
1,002 Documents
<![CDATA[Comparative Performance Evaluation of Linear, Bagging, and Boosting Models Using BorutaSHAP for Software Defect Prediction on NASA MDP Datasets ]]>
<![CDATA[Kartika, Najla Putri]]>;
<![CDATA[Herteno, Rudy]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Nugrahadi, Dodon Turianto]]>;
<![CDATA[Abadi, Friska]]>;
<![CDATA[Ahmad, Umar Ali]]>;
<![CDATA[Faisal, Mohammad Reza]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5393
<![CDATA[Software defect prediction aims to identify potentially defective modules early on in order to improve software reliability and reduce maintenance costs. However, challenges such as high feature dimensions, irrelevant metrics, and class imbalance often reduce the performance of prediction models. This research aims to compare the performance of three classification model groups—linear, bagging, and boosting—combined with the BorutaSHAP feature selection method to improve prediction stability and interpretability. A total of twelve datasets from the NASA Metrics Data Program (MDP) were used as test references. The research stages included data preprocessing, class balancing using the Synthetic Minority Oversampling Technique (SMOTE), feature selection with BorutaSHAP, and model training using five algorithms, namely Logistic Regression, Linear SVC, Random Forest, Extra Trees, and XGBoost. The evaluation was conducted with Stratified 5-Fold Cross-Validation using the F1-score and Area Under the Curve (AUC) metrics. The experimental results showed that tree-based ensemble models provided the most consistent performance, with Extra Trees recording the highest average AUC of 0.794 ± 0.05, followed by Random Forest (0.783 ± 0.06). The XGBoost model provided the best results on the PC4 dataset (AUC = 0.937 ± 0.008), demonstrating its ability to handle complex data patterns. These findings prove that BorutaSHAP is effective in filtering relevant features, improving classification reliability, and strengthening transparency and interpretability in the Explainable Artificial Intelligence (XAI) framework for software quality improvement.]]>
<![CDATA[Development of Mobile Quran App with Screen Time Monitoring Using DRM, Agile, and Sus-Use Testing ]]>
<![CDATA[Abdulhafidz, Yahya]]>;
<![CDATA[Zaky, Umar]]>;
<![CDATA[Admojo, Fadhila Tangguh]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5398
<![CDATA[The rapid growth of mobile applications has changed user behavior in the digital age, including how individuals interact with religious content. However, excessive use of social media has led to behavioral problems such as doom scrolling, zombie scrolling, and digital addiction, phenomena collectively known as “brain rot,” which negatively impact cognitive, emotional, and spiritual well-being. This study aims to develop and evaluate Quran Break, a mobile Quran application that integrates screen time monitoring as a digital behavior intervention to encourage users to stop scrolling and engage in reading the Quran. The methodology applies the Design Research Methodology (DRM) through four iterative stages, supported by an Agile development model with short, adaptive sprints that enable continuous feedback and improvement. 18 participants were involved in usability testing using the System Usability Scale (SUS) and the Usability, Satisfaction, Ease of Learning, and Ease of Use (USE) questionnaire. The results showed that the application achieved an average SUS score of 75 (Good) and a USE score of 87.7% (Very Good), indicating that Quran Break is effective, useful, and easy to use. This discovery contributes to the fields of Religious Informatics and Human-Computer Interaction (HCI) by integrating persuasive technology into faith-based digital systems, supporting digital well-being, and promoting a balanced interaction between technology use and spiritual activities.]]>
<![CDATA[An Interpretable Deep Learning Framework for Multi-Class Lung Disease Diagnosis Using ConvNeXt Architecture ]]>
<![CDATA[Basyir, Muhammad Khalidin]]>;
<![CDATA[Furqan, Mhd]]>;
<![CDATA[Fadlan, Aulia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5404
<![CDATA[Lung diseases remain a major global health challenge, requiring accurate and interpretable diagnostic systems to support timely detection and treatment. This study proposes a high-fidelity deep learning approach using the ConvNeXt architecture for automated multi-class classification of chest X-ray (CXR) images into five categories: Bacterial Pneumonia, Viral Pneumonia, COVID-19, Tuberculosis, and Normal. The methodology involved preprocessing 10.095 Kaggle-sourced images (normalization, CLAHE, augmentation, resizing) and training a ConvNeXt model for 70 epochs with the Adam optimizer. The model achieved strong performance with 92.66% validation accuracy, 86.32% test accuracy, a macro-average F1-score of 0.86, and a macro-average AUC of 0.99. Grad-CAM visualizations demonstrated the model's consistent focus on clinically relevant lung regions, significantly improving interpretability and clinical applicability. This study contributes to advancing interpretable AI methods for clinical decision support in medical imaging, offering a reliable and transparent framework for automated lung disease diagnosis.]]>
<![CDATA[Sentinel-2 NDVI Analysis Using GEE and QGIS for Green Open Space Sustainability Assessment in Kendari City ]]>
<![CDATA[Sufrianto, Sufrianto]]>;
<![CDATA[Yaacob Zubir, Siti Sara]]>;
<![CDATA[Jassin, Andi Makkawaru Isazarni]]>;
<![CDATA[Brata, Joko Tri]]>;
<![CDATA[Danggi, Erni]]>;
<![CDATA[Sallu, Sulfikar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5409
<![CDATA[Rapid urbanization has profoundly transformed land cover in many growing cities, leading to a substantial decline in Green Open Space (GOS) and a progressive deterioration of ecological functions. The continuous conversion of vegetated zones into impervious and built-up surfaces has reduced the city’s ability to absorb carbon, regulate local microclimates, and maintain overall ecological resilience. Consequently, assessing the sustainability and spatial distribution of GOS is crucial for ensuring environmentally balanced urban development and resilience to future land-use pressures. This study aims to evaluate the sustainability of urban green spaces in Kendari City through an integrated geospatial approach that combines remote sensing and open-source cloud computing technologies. Sentinel-2 Level-2A imagery was analyzed in Google Earth Engine (GEE) using the QA60 band for cloud masking and spatial clipping to accurately define the study boundaries. Normalized Difference Vegetation Index (NDVI) values were subsequently processed and classified in QGIS using a reclassification technique to distinguish vegetation density categories. The results indicate that 56.7% of the total land area, equivalent to 15,213 hectares, exhibits high greenness, reflecting dense and healthy vegetation, whereas 32.3% consists of low or non-vegetated surfaces dominated by built-up and barren lands. These findings reveal substantial spatial disparities in vegetation coverage and underscore the importance of sustainable land management and green infrastructure policies. Furthermore, this research contributes to the advancement of geospatial informatics by developing an open, reproducible workflow that integrates cloud-based computation and open-source GIS for urban ecological monitoring and sustainability assessment.]]>
<![CDATA[Interpretable Hybrid YOLOv8s-GWO Framework for Bounding-Box Viral Pneumonia Detection on Kaggle Chest X-ray Images ]]>
<![CDATA[Jalaluddin Amron, Azmi]]>;
<![CDATA[Paramita, Cinantya]]>;
<![CDATA[Šolić, Petar]]>;
<![CDATA[Supratiknyo, Supratiknyo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5419
<![CDATA[Viral pneumonia continues to impose a substantial global health burden, making rapid and reliable radiographic detection essential for early clinical management. This study proposes a hybrid framework integrating the YOLOv8s detection model with the Grey Wolf Optimizer (GWO) to enhance hyperparameter tuning for Viral Pneumonia identification in chest X-ray images. A curated set of Normal and Viral Pneumonia samples was manually annotated and preprocessed before training. The optimization process involved multi-stage refinement of learning rate, momentum, weight decay, and loss-gain parameters to improve convergence stability and detection accuracy. The optimized YOLOv8s + GWO model demonstrated notable performance gains, achieving 0.965 recall, 0.983 mAP@50, and 0.827 mAP@50–95 on internal evaluations. External testing further validated its robustness, delivering 98.80% accuracy, 99.48% specificity, and 97.46% sensitivity. These results highlight not only enhanced clinical diagnostic reliability but also contributions to Informatics and Computer Science, demonstrating the effectiveness of metaheuristic-guided optimization in improving deep-learning model performance, generalization, and computational efficiency for AI-driven image detection tasks.]]>
<![CDATA[Predictive Modeling for Underweight Detection in Toddlers Using Support Vector Machine, K-Nearest Neighbors, and Decision Tree C4.5 Algorithms ]]>
<![CDATA[Ekowati, Maria Atik Sunarti]]>;
<![CDATA[Hidayat, Nurul]]>;
<![CDATA[Karim, Abdul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5439
<![CDATA[Gizi kurang (underweight) pada balita masih menjadi tantangan utama kesehatan masyarakat di Indonesia, dengan prevalensi mencapai 15,9% berdasarkan Survei Kesehatan Indonesia tahun 2023. Kondisi ini berdampak serius terhadap pertumbuhan fisik, perkembangan kognitif, dan kualitas hidup anak. Penelitian ini bertujuan untuk mengembangkan model prediktif guna mendeteksi dini status gizi balita dengan menggunakan metode supervised machine learning. Tiga algoritma pembelajaran terawasi diterapkan dan dievaluasi, yaitu Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Decision Tree C4.5, dengan memanfaatkan dataset berisi 9.284 catatan balita dari Kabupaten Sukoharjo yang mencakup delapan atribut dan satu label kelas status gizi. Hasil analisis menunjukkan bahwa algoritma SVM memberikan performa klasifikasi tertinggi dengan akurasi 98,56%, diikuti KNN dengan akurasi 97,99% dan Decision Tree C4.5 dengan akurasi 96,96%. Temuan ini menegaskan bahwa machine learning dapat menjadi alat yang efektif untuk identifikasi dini risiko gizi kurang pada anak, sehingga memungkinkan intervensi yang lebih cepat, tepat, dan berbasis data. Pendekatan ini berkontribusi pada peningkatan efektivitas program kesehatan anak dan mendukung pencapaian target pembangunan kesehatan nasional.]]>
<![CDATA[A Hybrid Deep Learning Architecture for Cost-Effective, Real-Time IV Infusion Anomaly Detection using IoT Sensors ]]>
<![CDATA[Brian Nafis, Muhammad]]>;
<![CDATA[Paramita, Cinantya]]>;
<![CDATA[Wright , Sasha-Gay]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5440
<![CDATA[Intravenous (IV) infusion therapy is a critical medical procedure, yet manual monitoring increases the risk of complications such as air embolism and irregular infusion flow, particularly in resource-constrained environments. Although several automated infusion monitoring systems have been proposed, their high implementation cost limits practical adoption. This research develops a low-cost IoT-based infusion monitoring system capable of real-time anomaly detection using a multi-architecture machine learning approach. The proposed prototype integrates an ESP32 microcontroller with load cell (HX711) and optical (LM393) sensors to acquire time-series infusion data. Ten models from classical machine learning, deep learning, hybrid, and ensemble categories were evaluated using a dataset of 10,420 records under a unified experimental setup. The results show that XGBoost had a perfect recall (1.0000) and a strong PRAUC, while the LSTM Autoencoder had the highest F1-Score (0.9343) and precision (0.8934). The best overall performance came from hybrid and ensemble methods, with CNN–LSTM having an F1-Score of 0.89, a recall of 0.99, and a precision of 0.80. This means they would be great for clinics where being sensitive is very important. The research shows that using a low-cost IoT infrastructure with carefully chosen deep learning or ensemble models can help find problems in real time. A web dashboard explains how the technology operates and its capabilities. This study examines a cost-effective and easily scalable method to enhance infusion safety in hospitals with limited financial resources.]]>
<![CDATA[Incremental CNN-k-NN Hybrid Facial Recognition for Helmeted Facial Recognition in IoT-Enabled Smart Parking: A Case Study at Universitas Mataram ]]>
<![CDATA[Widiartha, Ida Bagus Ketut]]>;
<![CDATA[Husodo, Ario Yudo]]>;
<![CDATA[Thuy, Tran Thi Thanh]]>;
<![CDATA[Murpratiwi, Santi Ika]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5447
<![CDATA[Helmeted rider identification challenges traditional facial recognition, especially in Indonesian campuses like UNRAM, where motorbike use is prevalent and theft risks are high. This study develops a hybrid CNN-k-NN system for secure parking access. The dataset contains 2,800 augmented images (Haar Cascade crop, 224x224 grayscale), with features extracted via VGG16/ResNet and classified using k-NN (k=1, Euclidean/Cosine). The system achieves 95.62% accuracy, with precision, recall, and F1 scores of 0.96. Incremental retraining reduces processing time to under 1 second, compared to 30 minutes for full retraining. The use of cosine similarity improves accuracy slightly over Euclidean distance. This solution enhances IoT-based smart campuses by enabling efficient, real-time identification and reducing theft by improving access control. It is adaptable to low-resource environments, supporting scalable deployments in smart parking and campus security systems.]]>
<![CDATA[Proximal Policy Optimization for Adaptive Resource Allocation in Mobile OS Kernels: Enhancing Multitasking Efficiency ]]>
<![CDATA[Machmudi, Moch. Ali]]>;
<![CDATA[Putra, Yusuf Wahyu Setiya]]>;
<![CDATA[Naim, Abdul Ghani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5448
<![CDATA[Traditional mobile operating system (OS) schedulers struggle to maintain optimal performance amidst the increasing complexity of user multitasking, often resulting in significant latency and energy waste. This study aims to integrate a Proximal Policy Optimization (PPO) based Reinforcement Learning (RL) framework for predictive and adaptive resource allocation. Methodologically, we formulate the scheduling problem as a Markov Decision Process (MDP) where States (S) encompass CPU load, memory usage, and workload patterns; Actions (A) involve dynamic core affinity, frequency scaling, and cgroup adjustments; and Rewards (R) are calculated based on a weighted trade-off between performance maximization and energy conservation. A PPO actor-critic network is implemented and trained on a modified Android kernel (discount factor γ=0.99) under simulated high-load scenarios, including simultaneous video conferencing, data downloading, and web browsing. Experimental results demonstrate that the proposed RL mechanism reduces average task latency by 18% and boosts system responsiveness by 25%, while simultaneously achieving a 12% reduction in CPU power consumption compared to the baseline scheduler. These findings pioneer intelligent OS informatics, offering a robust foundation for sustainable multitasking for over a billion Android users through scalable, on-device fine-tuning.]]>
<![CDATA[Analyzing User Needs and Recommending Targeted Features for Bi’ih Village Tourism Website Using Text Mining and K-Means Clustering ]]>
<![CDATA[Artamevia, Mima]]>;
<![CDATA[Lubis, Muharman]]>;
<![CDATA[Mukti, Iqbal Yulizar]]>;
<![CDATA[Handayani, Dini]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5458
<![CDATA[Tourism village websites often do not fully reflect user needs, resulting in digital services that cannot be optimally utilized by residents and potential tourists. This situation limits access to information and reduces the effectiveness of tourism promotion efforts, especially in villages that are undergoing digital transformation. This study was conducted to identify the overall needs of users and compile data-based feature recommendations for the development of the Bi'ih Village website as a durian tourism village. The research method used a quantitative approach through the distribution of an online questionnaire to 110 respondents consisting of visitors and residents, with five open-ended questions and several structured questions. The data was analyzed using text mining to find dominant words and themes, as well as the K-Means Clustering technique determined through the Elbow method to group user characteristics. The analysis results showed that there were 2,702 tokens and 677 meaningful words, with the highest demand for government information and visual tourism content. The segmentation process produced three main groups, namely Active Supporters (61.4%), Tech Enthusiasts (27.3%), and Moderate Users (11.4%). This study contributes a data-driven approach to designing more relevant and measurable features for tourism village websites. The impact is expected to increase the adoption of village digital services, strengthen tourism competitiveness, and support the acceleration of the Smart Village concept implementation. The novelty of this study lies in the integration of text mining and clustering as the basis for developing user-oriented feature recommendations.]]>
