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[Multi-Class Brain Tumor Segmentation and Classification in MRI Using a U-Net and Machine Learning Model ]]>
<![CDATA[Hendrik, Jackri]]>;
<![CDATA[Pribadi, Octara]]>;
<![CDATA[Hendri, Hendri]]>;
<![CDATA[Hoki, Leony]]>;
<![CDATA[Tarigan, Feriani Astuti]]>;
<![CDATA[Wijaya, Edi]]>;
<![CDATA[Ali, Rabei Raad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5369
<![CDATA[Brain tumor diagnosis remains a critical challenge in medical imaging, as accurate classification and precise localization are essential for effective treatment planning. Traditional diagnostic approaches often rely on manual interpretation of MRI scans, which can be time-consuming, subjective, and prone to variability across radiologists. To address this limitation, this study proposes a two-stage framework that integrates machine learning (ML) based classifiers for tumor type recognition and a U-Net architecture for tumor segmentation. The classifier was trained to distinguish four tumor categories: glioma, meningioma, pituitary, and no tumor, while the U-Net model was employed to delineate tumor regions at the pixel level, enabling volumetric assessment. The novelty of this research lies in its dual focus that combines classification and segmentation within a single framework, which enhances clinical applicability by offering both diagnostic and spatial insights. Experimental results demonstrated that among the evaluated classifiers, XGBoost achieved the highest accuracy of 86 percent, surpassing other models such as Random Forest, SVC, and Logistic Regression, while the U-Net model delivered consistent segmentation performance across tumor types. These findings highlight the potential of hybrid ML and deep learning solutions to improve reliability, efficiency, and objectivity in brain tumor analysis. In real-world practice, the proposed framework can serve as a valuable decision-support tool, assisting radiologists in early detection, reducing diagnostic workload, and supporting personalized treatment strategies.]]>
<![CDATA[Decision Support System for Selecting Outstanding Religious Counselors in Jambi Province Using Analytical Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution ]]>
<![CDATA[Suryani, Suryani]]>;
<![CDATA[Zaenal Abidin, Dodo]]>;
<![CDATA[Purnama, Benni]]>;
<![CDATA[Gunardi, Gunardi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5385
<![CDATA[Religious counselors play an essential role in fostering religious moderation, strengthening community cohesion, and promoting social harmony. However, the evaluation of their performance remains largely manual, leading to subjectivity, inconsistency, and limited accountability. This study develops a web-based Decision Support System that integrates the Analytical Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution to enhance objectivity, transparency, and data-driven evaluation. The Analytical Hierarchy Process was applied to determine the importance of five criteria—portfolio, scientific paper, program video, presentation or interview, and absenteeism—through expert pairwise comparisons. The Technique for Order Preference by Similarity to Ideal Solution was then used to rank twenty-four religious counselors from the Regional Office of the Ministry of Religious Affairs in Jambi Province. The results show that portfolio (47.4%) and presentation or interview (24.4%) were the most influential criteria, while the others served as complementary factors. Counselors with comprehensive documentation and strong communication skills consistently ranked higher, validating the system’s analytical reliability. This study’s novelty lies in applying a multi-criteria decision-making framework within the religious sector, directly aligned with the 2024 Technical Guidelines for the Islamic Religious Counselor Award (Keputusan Dirjen Bimas Islam No. 352/2024). Furthermore, this research supports the Ministry of Religious Affairs’ Eight Priority Transformation Programs (Asta Protas), particularly in digitalizing governance and promoting transparent, accountable, and data-driven management. From an informatics perspective, this system demonstrates the effective implementation of decision-support algorithms in a web-based environment, highlighting the contribution of information technology to evidence-based performance evaluation.]]>
<![CDATA[Aligning Software Architecture with Cost Structure: A Comparative Study Using ATAM and Lean Canvas in Early Startup Development ]]>
<![CDATA[Fadhillah, Jan Falih]]>;
<![CDATA[Kusumo, Dana Sulistiyo]]>
<![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.2025.6.6.4302
<![CDATA[Startups in the early phase often face challenges in balancing operational efficiency with resource constraints. This research find how startups can choose software architecture to align with cost structures with the Lean Canvas framework and the Architecture Trade-off Analysis Method (ATAM). Lean canvas allows for startups to identify cost structures at an early stage and align with market demands efficiently and ATAM helps to evaluate software architecture systematically by analysing trade-offs and quality attributes. Although microservice architecture offers modularity and scalability, its implementation can lead to higher operational costs making it unsuitable for startups with limited budgets. On the other hand, monolithic architecture is more cost-effective, easy to manage and suitable for the needs of early-stage startups. This research emphasizes that systematic evaluation of software architecture based on business goals and resource limitations is essential for startup growth for sustainability. By combining Lean Canvas for business validation and ATAM for architectural decision making, startups can optimize operational and technical strategies, analyse risks, and identify trade-offs that are implemented according to business development.]]>
<![CDATA[Efficient Waste Classification in Cisadane River Using Vision Transformer and Swin Transformer Architectures ]]>
<![CDATA[Surahmat, Asep]]>;
<![CDATA[Mutiarawan, Rezza Anugrah]]>
<![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.2025.6.6.4451
<![CDATA[The increasing volume of waste in rivers has become a serious environmental problem. This study proposes the implementation of Artificial Intelligence (AI)-based models, specifically Vision Transformer (ViT) and Swin Transformer, for an automatic waste sorting system in the Cisadane River, Tangerang. The dataset used combines public sources and field data, processed through preprocessing and augmentation to improve robustness. Model training was conducted using k-fold cross-validation, pruning, and deployment testing on edge devices to ensure generalization and efficiency. Several architectural innovations were introduced, including Dynamic Patch Size for adapting to various waste shapes and sizes, and Spatial-Aware Attention to enhance focus on waste objects against complex river backgrounds. The evaluation involved a confusion matrix and statistical analysis using a paired t-test to validate the significance of the results. Experimental findings show that Swin Transformer achieved the highest accuracy of 94.2%, surpassing ViT at 91.8%, with precision of 93.5%, recall of 92.7%, and F1-score of 93.1%. Swin Transformer also proved more reliable in dynamic lighting and cluttered environments. This study demonstrates the potential of Transformer-based architectures in automatic waste classification, contributing to smarter and more efficient AI-based environmental management technologies.]]>
<![CDATA[Labeling Optimization and Hybrid CNN Model in Sentiment Analysis of Movie Reviews with Slang Handling ]]>
<![CDATA[Saputra, Alfin Nur Aziz]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Saputra, Dhanar Intan Surya]]>
<![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.2025.6.6.4465
<![CDATA[This research focuses on the development of a hybrid Convolutional Neural Network (CNN) model for sentiment analysis of movie comments, specifically designed to overcome the challenges of handling nonstandard language and slang. Slang is often an obstacle in sentiment analysis due to its non-standard nature and is difficult to recognize by traditional algorithms. By utilizing an kamusalay as a data preprocessing step, this research successfully converts slang words into standardized forms, thus improving the quality of data used in modeling. The data was collected through YouTube Data API on the comments of the movie “Pengabdi Setan 2: Communion” and processed using tokenization, stemming, stopwords removal, and TF-IDF feature extraction techniques. The hybrid model combines machine learning algorithms such as Naive Bayes, Logistic Regression, and Random Forest with CNN's ability to extract complex spatial patterns from text data. The evaluation results show that this model is able to achieve up to 95% accuracy, with consistently high precision, recall, and F1-score. This approach not only improves the accuracy of sentiment analysis, but also provides an effective solution for handling non-standard language variations, making it relevant for application in digital opinion analysis on social media.]]>
<![CDATA[Optimizing Early Network Intrusion Detection: A Comparison of LSTM and LinearSVC with SMOTE on Imbalanced Data ]]>
<![CDATA[Nugroho, Khabib Adi]]>;
<![CDATA[Hariguna, Taqwa]]>;
<![CDATA[Barkah, Azhari Shouni]]>
<![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.2025.6.6.4672
<![CDATA[This study aims to improve network intrusion detection systems (IDS) by addressing class imbalance in the CICIDS 2017 dataset. It compares the effectiveness of Long Short-Term Memory (LSTM) networks and Linear Support Vector Classifier (LinearSVC) in detecting intrusions, with a focus on the impact of Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset. The dataset was preprocessed by removing irrelevant features, handling missing values, and applying Min-Max normalization. SMOTE was applied to balance the training dataset. Results showed that LSTM outperformed LinearSVC, especially in recall and F1-score, after applying SMOTE. This research highlights the benefits of combining LSTM with SMOTE to address class imbalance in IDS and emphasizes the importance of temporal sequence models like LSTM for detecting network intrusions. Future work could involve using the full dataset, exploring advanced feature engineering, and implementing more complex architectures to further enhance performance. This research underscores the critical need for improving network security by addressing the challenges of class imbalance in intrusion detection systems, which is vital for ensuring the real-time identification and mitigation of sophisticated cyber threats in the ever-evolving landscape of network security.]]>
<![CDATA[Improving Detection Accuracy of Network Intrusions Using a Hybrid Network Intrusion Detection System Based on Isolation Forest and Random Forest Algorithms ]]>
<![CDATA[Wang, Ryan Christensen]]>;
<![CDATA[Avrianto, Refgiufi Patria]]>
<![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.2025.6.6.4694
<![CDATA[The growing sophistication of cyberattacks has increased the urgency of securing organizational networks, especially those handling sensitive and large-scale data. Traditional intrusion detection systems (IDS) such as Suricata rely on signature-based methods and often fail to detect zero-day or evolving threats. To address this gap, this research proposes a hybrid intrusion detection model that integrates Suricata with machine learning algorithms—Isolation Forest and Random Forest. Suricata performs real-time packet inspection and anomaly filtering, while the machine learning component enhances detection of novel threats and reduces false positives. The methodology involves capturing real-time network traffic, pre-processing data, training models on both CICIDS2017 and simulated attack data, and evaluating performance using accuracy, precision, recall, and F1-score. Experimental results show that the hybrid model achieves high detection accuracy—99.86% on simulated data and 96.33% on the CICIDS2017 dataset. Compared to standalone Suricata, the hybrid model detects more unknown threats and reduces alert fatigue by minimizing false positives. This study contributes a scalable and adaptive IDS framework that combines anomaly- and signature-based detection techniques. The proposed system enhances threat detection capabilities in enterprise-level networks and offers practical implications for intelligent cybersecurity defences. The findings advance research in computer science, particularly in the domains of machine learning applications and network security systems.]]>
<![CDATA[Validation of Question Classification Using Support Vector Machine and Intraclass Correlation Coefficient Based on the Revised Bloom’s Taxonomy ]]>
<![CDATA[Darfiansa, Lazuardy Syahrul]]>;
<![CDATA[Larasti, Sza Sza Amulya]]>
<![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.2025.6.6.4728
<![CDATA[The assessment process must be carried out accurately as it is a crucial aspect of identifying cognitive abilities in students. Cognitive ability identification needs to be done by providing exam questions that refer to the Revised Bloom's Taxonomy for difficulty-level classification to ensure students' understanding of what has been taught. The traditional manual classification process carried out by educators often requires significant time and is susceptible to subjective variability. The classification of questions from levels C1 to C6 based on the Revised Bloom's Taxonomy shows an imbalance in the data distribution for each level, leading to inaccurate classification results. The automatic classification technique using the SVM algorithm allows educators to quickly classify questions based on their difficulty levels. The automated classification technique needs to be validated to what extent the difficulty levels classified by the machine align with the perceptions of educators and students. This research will validate the results of question classification generated from the SVM algorithm, supplemented by the oversampling technique to address data imbalance. The validation method used is ICC. Applying the SMOTE oversampling technique to handle a class imbalance in the training data shows improvement, with an accuracy rate of 91% when using SMOTE compared to 83% without it. Results of the classification suitability test with the SVM algorithm by educators and students indicate a high level of agreement. The ICC Average Measures values are as follows: SVM classification is 0,979, assessment by non-science subject educators is 0,956, assessment by science subject educators is 0,991, assessment by non-science subject students is 0,982, and assessment by science subject students is 0,984. ICC testing consistently yields excellent results in non-science and science subjects, indicating that the assessments conducted by educators and students have a very high level of agreement.]]>
<![CDATA[Optimization Strategy for Electric Vehicle Charging Station Development at Gas Stations Using GIS-AHP-SAW Framework ]]>
<![CDATA[Saputra, Andika Jaka]]>;
<![CDATA[Suharjito, Suharjito]]>
<![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.2025.6.6.4744
<![CDATA[The rapid adoption of electric vehicles (EVs) requires the acceleration of electric vehicle charging station (EVCS) development. However, selecting optimal locations for EVCS development remains challenging. The EVCS Infrastructure Standard emphasizes that technical factors are essential, which aligns with earlier studies that point out the need to consider technical requirements along with sustainability criteria. This study aims to identify a novel optimization strategy for the EVCS development at gas stations, utilizing both technical and sustainability factors. We identified the gas station as an alternative site, conforming to regulatory guidelines and prior studies. This Framework integrates GIS, AHP, and SAW methods to achieve the research objectives. We evaluated the framework using suitability analysis, mathematical optimization techniques and conducted empirical study in a designated region of Indonesia to assess the practical applicability. The study's revealed substantial findings and efficient optimization strategies. The power network subcriterion ranking as the most critical in the hierarchy of criteria. The GS05 and GS22 locations attain an optimal level across all optimization scenarios. The improved accessibility of power network facilities can augment the total alternative weight by 22.5% and improve the coverage demand from 6% to 47%. The results indicated the optimization strategy focused on improving electricity network facilities at the gas station is the best strategy for EVCS Development. This framework demonstrated a replicable model for decision support systems within the domain of spatial informatics and smart infrastructure planning, specifically spatial decision support systems for EV infrastructure planning, and offers valuable insights for investor decision-making.]]>
<![CDATA[Stroke Risk Prediction using Winsorizing Interquartile Range and Tree-Based Classification with Explainable Artificial Intelligence ]]>
<![CDATA[Rahmadani, Fitria]]>;
<![CDATA[Wiharto, Wiharto]]>;
<![CDATA[Zuhdi, Shaifudin]]>
<![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.2025.6.6.4760
<![CDATA[According to the Global Burden of Disease (GBD) Study, stroke is the third leading cause of death globally. Recognizing its signs early is crucial for both prevention and effective treatment. Although machine learning has made significant progress in predicting strokes, many current models operate like "black boxes", making them hard to interpret and often resulting in high error rates. This study aims to enhance prediction accuracy and interpretability in stroke risk detection by integrating Winsorizing Interquartile Range (IQR) for outlier management, a tree-based classification method, and Explainable Artificial Intelligence (XAI) techniques. The proposed approach applies Winsorizing Interquartile Range to handle extreme values while employing tree-based methods for prediction due to their superior performance in processing tabular data. Additionally, Explainable Artificial Intelligence techniques are utilized to improve model transparency and interpretability. Testing was conducted using the Cerebral Stroke Prediction-Imbalanced Dataset, comparing results with various existing models. The suggested approach demonstrated the lowest prediction error rates, achieving a False Positive Rate (FPR) of 15.74% and a False Negative Rate (FNR) of 8.56%. Additionally, it attained an accuracy of 84.39%, sensitivity of 91.43%, specificity of 84.26%, Area Under the Receiver Operating Characteristic Curve (AUROC) of 94.74%, and G-Mean of 87.76%, outperforming previous studies in stroke risk prediction. The combination of Winsorizing Interquartile Range, Random Under-Sampling, tree-based classification, and Explainable Artificial Intelligence techniques effectively enhances prediction accuracy and transparency, supporting early stroke detection with improved interpretability. This study contributes to medical informatics by integrating transparent predictive models suitable for decision support systems.]]>
