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
1,111 Documents
<![CDATA[Geographic Information System for Land Suitability Mapping of Partner Farmers at Okiagaru Indonesia Agricoop Using Rule-Based System and Prototype Methodology ]]>
<![CDATA[Wicaksono, Aditya]]>;
<![CDATA[Manalu, Doni Sahat Tua]]>;
<![CDATA[Sebayang, Veralianta Br]]>;
<![CDATA[Pratama, Agief Julio]]>;
<![CDATA[Pamungkas, Muhammad Aldryansyah]]>;
<![CDATA[Puspa, Amelia Setya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5471
<![CDATA[Geographic Information System (GIS) for land suitability assessment integrates spatial and attribute data to evaluate and map areas for food crops and horticulture. The system applies parameters such as temperature, rainfall, water pH, clay CEC, organic carbon, and other soil characteristics, analyzed through a rule-based approach. Its main goal is to optimize agricultural land use by aligning crop selection with physical and environmental conditions. GIS-based analysis enables accurate digital mapping and categorizes land into highly suitable, moderately suitable, marginally suitable, and unsuitable classes, providing valuable insights for farmers, governments, and stakeholders in sustainable land management. System development employed the Prototype methodology, emphasizing iterative stages of requirement gathering, rapid design, prototype construction, user evaluation, and refinement. The Land Suitability GIS (SigKL) was tested at five partner-farmer sites in Cianjur and Sukabumi. Black Box Testing confirmed that all 20 functional features achieved a 100% success rate. The system supports the identification of potential new agricultural areas and offers recommendations for improving less productive land. The novelty of this research lies in integrating FAO (1976) classification with interactive digital mapping and locally tailored knowledge rules, enabling real-time accessibility. Unlike prior studies limited to static analysis, SigKL introduces an adaptive, rule-based GIS prototype with interactive visualization, directly supporting decision-making for sustainable agriculture. This innovation enhances transparency and accessibility, contributing to the Sustainable Development Goals (SDGs) related to food security and sustainable farming.]]>
<![CDATA[Rule-Based Expert System for Personalized GERD Food Recommendations Using Forward Chaining and Certainty Factor in Indonesia ]]>
<![CDATA[Maulia, Alifani]]>;
<![CDATA[Purwadi, Purwadi]]>;
<![CDATA[Kusuma, Bagus Adhi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5477
<![CDATA[This study develops a personalized and transparent rule-based expert system to support dietary decision-making for Indonesian patients with gastroesophageal reflux disease (GERD), addressing a critical gap in existing expert-system applications that focus mainly on diagnosis rather than daily diet management. The system integrates knowledge derived from the Indonesian GERD Consensus (2022) and its 2024 addendum with local nutritional evidence to construct if–then rules that classify foods into safe, limit, and avoid categories. A forward-chaining inference engine processes user-specific inputs—including symptoms, trigger sensitivities, eating behaviors, and dietary restrictions—while the Certainty Factor (CF) model quantifies confidence levels to accommodate individual tolerance variability. The system was implemented using Python and deployed through a Gradio-based wizard interface, enabling stepwise data collection and producing Top-N food recommendations with explainable “reason traces.” Functional evaluations across mild, moderate, and severe profiles demonstrated consistent alignment with national dietary guidelines, steering users toward low-fat, non-spicy, soft-textured, and clear-broth menu options, while eliminating high-risk trigger foods. Preliminary expert validation indicated high agreement with guideline principles, emphasizing the system’s interpretability and practical relevance. This research contributes to the field of health informatics by operationalizing forward chaining and CF for personalized dietary support, offering an auditable and computationally efficient alternative to black-box recommendation systems. Future developments include expanding the food dataset, refining CF calibration, and conducting structured clinical validation to enhance performance and applicability in real-world mHealth environments.]]>
<![CDATA[Geodetically-Enhanced Hybrid GRU with Adaptive Dropout and Dynamic L2 Regularization for Earthquake Parameter Prediction in Indonesia ]]>
<![CDATA[Mubarak MR, Najmuddin]]>;
<![CDATA[Susandri, Susandri]]>;
<![CDATA[Zamsuri, Ahmad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5478
<![CDATA[Earthquake prediction remains challenging due to the nonlinear behavior and uncertainty of seismic activity. This study introduces a geodetically-enhanced hybrid GRU model integrating adaptive dropout and dynamic L2 regularization to improve robustness and accuracy in earthquake magnitude prediction. In addition to seismic sequence data, slip-rate values derived from scalar moment distribution were incorporated as a domain-informed feature to represent tectonic strain accumulation across Indonesia. The dataset consisted of BMKG records from 2010–2025 and was processed through outlier removal, normalization, temporal reshaping, and feature integration. The proposed model was evaluated against multiple deep learning baselines including CNN-1D, LSTM, standard GRU, Transformer-based models, and Neural ODE architectures. Performance assessment used RMSE, MAE, and R² metrics. The resulting hybrid GRU achieved improved predictive accuracy with an RMSE of 0.5176, MAE of 0.3973, and an R² score of 0.5997, outperforming both CNN-1D and standard GRU baselines. The integration of slip-rate features contributed to reduced prediction variance across tectonically active zones. These findings demonstrate that combining geodetic information with adaptive regularization strategies improves generalization and model stability for seismic forecasting. The approach offers potential applicability for rapid early-warning scenarios requiring low latency and reliable prediction accuracy.]]>
<![CDATA[Document Verification And Authentication By Using Password Based Qr Code Signature With Rsa 2048, Aes Encryption, And Sha-256 ]]>
<![CDATA[Adithia, Mariskha Tri]]>;
<![CDATA[Elianora, Naomi]]>;
<![CDATA[Veronica, Maria]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5484
<![CDATA[This research presents a secure digital-signature framework for document authentication using QR Codes, combining three modern cryptographic primitives: RSA 2048-bit for digital signing, SHA-256 for document-integrity verification, and password-based AES encryption to protect the signer’s private key. The system addresses a recurring limitation in previous QR-Code-based signature schemes—the absence of secure private-key storage—by deriving AES keys from user passwords and salts, ensuring that RSA private keys are never stored in plaintext. A web-based implementation was developed to support user registration, signature generation, and document verification, requiring only a PDF file and the associated password from users. Functional testing demonstrates that the system accurately authenticates signer identities, detects any modification to document content, identifies incorrect document numbers, and rejects invalid or non-signature QR Codes. These results confirm that the combination of RSA 2048, SHA-256 hashing, and password-derived AES encryption effectively ensures confidentiality of private keys while preserving document integrity and authenticity. The approach also prevents common forgery scenarios, including document substitution, unauthorized content changes, and QR Code misuse.]]>
<![CDATA[Optimization of MobileNet SSD Using Pruning, Quantization, and Transfer Learning for Real-Time Vehicle Detection in IoT-Based Security Systems ]]>
<![CDATA[Miranto, Afit]]>;
<![CDATA[Prasetyawan, Purwono]]>;
<![CDATA[May Aryanto, Iqbal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5488
<![CDATA[Security is a critical requirement in modern public and private environments, especially in systems that rely on resource-constrained IoT devices. This research aims to optimize the MobileNet SSD (Single Shot MultiBox Detector) model to achieve fast and reliable real-time vehicle and human detection on low-power hardware. The proposed optimization pipeline integrates three techniques: pruning to reduce network redundancy, quantization to accelerate inference and decrease memory usage, and transfer learning using six relevant object classes (person, car, motorcycle, bicycle, bus, and truck). Experiments were conducted on a Raspberry Pi 5 equipped with a camera and local dashboard interface. The optimized MobileNet SSD v2 model achieved a mean Average Precision (mAP) of 0.724 and mAP@0.5 of 0.951, while improving inference speed from 21 FPS to over 24 FPS. These results indicate a balanced trade-off between accuracy, speed, and resource efficiency, enabling stable real-time performance on constrained IoT platforms. The findings contribute to the body of knowledge in embedded and edge AI by demonstrating how integrated model-level optimization can significantly enhance deep learning inference on low-power systems, offering scientific and practical implications for smart surveillance and intelligent traffic monitoring.]]>
<![CDATA[Bidirectional Cross-Attention and Uncertainty-Aware Ensemble for High-Precision Brain Tumor Classification on MRI Images ]]>
<![CDATA[Saputra, Gunawan Cholis]]>;
<![CDATA[Yuwanandra Risdyaksa, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5494
<![CDATA[Accurate brain tumor diagnosis via Magnetic Resonance Imaging (MRI) is vital for effective neuro-oncological treatment. Although CNNs are widely regarded as the benchmark for local texture extraction, they frequently exhibit limitations in modeling long-distance global dependencies effectively. In contrast, Vision Transformers (ViTs), particularly the Swin variant, demonstrate superior capability in capturing global semantic context yet often fail to preserve the fine local granularity needed to delineate tumor boundaries significantly. To bridge this gap, we propose Bi-CA-UAE, a hybrid framework integrating Swin Transformer and EfficientNet-V2 through a novel Bidirectional Cross-Attention mechanism. Unlike static ensembles, our method enables dynamic information exchange between global and local feature maps before classification. Furthermore, we introduce an Uncertainty-Aware Gating Network to adaptively weigh each branch based on prediction confidence, reducing false positives in ambiguous cases. Validated on a multi-class MRI dataset of 7,023 images, the model achieved 99.85% accuracy and an Expected Calibration Error (ECE) of 0.02, matching the strongest baseline (Swin Transformer) while demonstrating superior training stability and calibration. Unlike naive concatenation ensembles that suffered from overfitting and performance degradation in later training stages, Bi-CA-UAE maintained robust peak performance. Additionally, the model attained perfect recall (1.00) for Glioma and a micro-average AUC of 1.00. t-SNE visualizations and reliability diagrams confirm the model's ability to learn highly discriminative and well-calibrated features, positioning it as a trustworthy clinical decision support system.]]>
<![CDATA[Mixed-Data K-Means Clustering with Hyperparameter-Tuned Random Forest for OSS-Based MSME Investment Profiling and Policy Targeting ]]>
<![CDATA[Sari, Laura]]>;
<![CDATA[Maharrani, Ratih Hafsarah]]>;
<![CDATA[Hastuti, Hety Dwi]]>;
<![CDATA[Ramadhan, Adrian Putra]]>;
<![CDATA[Windasari, Wahyuni]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5545
<![CDATA[Administrative data of Micro, Small, and Medium Enterprises collected through the Online Single Submission system are highly heterogeneous, combining numerical and categorical attributes that hinder conventional investment segmentation and early-stage policy mapping. This study aims to develop a predictive clustering framework for enterprise investment profiling using mixed-type administrative data. The proposed methodology applies robust preprocessing, including RobustScaler for numerical variables and one-hot encoding with singular value decomposition for categorical features. Mixed-type similarity is computed using Gower distance, followed by a hybrid Gower–K-Means clustering approach, where the optimal number of clusters (k = 3) is determined using Silhouette, Calinski–Harabasz, and Davies–Bouldin indices. A comparative evaluation of clustering algorithms is conducted, with K-Prototypes performing best in the initial assessment and K-Means achieving superior performance after optimization. Cluster membership is subsequently predicted using a Random Forest classifier with hyperparameters optimized through randomized search. Experiments on 20,857 enterprise records identify three distinct clusters representing low-capital micro enterprises, transitional firms, and asset-intensive corporate entities. The optimized K-Means model achieves a Silhouette score of 0.97 and a Davies–Bouldin Index of 0.54. Compared with the untuned baseline, the tuned Random Forest model improves recall from 0.25 to 0.75 (200% increase) and increases the F1-score from 0.40 to 0.86 (114% improvement), while achieving 99.89% accuracy. These gains correspond to an estimated 20–30% improvement in MSME investment mapping effectiveness compared with traditional profiling approaches, providing a scalable AI-based blueprint for targeted regional economic governance.]]>
<![CDATA[Deep Convolutional Generative Adversarial Network-Enhanced Data Augmentation for Imbalance Facial Acne Severity Classification Using a Fine-Tuned EfficientNet-B1 ]]>
<![CDATA[Nisya, Khoirun]]>;
<![CDATA[Surono, Sugiyarto]]>;
<![CDATA[Thobirin, Aris]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5548
<![CDATA[Imbalanced datasets often hinder the generalization capability of Convolutional Neural Networks (CNNs) in medical image classification, leading to overfitting and reduced performance on minority classes. This study aims to develop an acne severity classification model using EfficientNet-B1 combined with geometric and photometric augmentation, as well as and Deep Convolutional Generative Adversarial Network (DCGAN)-based augmentation to address class imbalance. The dataset consists of 1,380 facial images categorized into four acne severity levels: Normal, Level 0, Level 1, and Level 2. Preprocessing includes RGB conversion, bilinear resizing, and center cropping. The data are split into training (80%), validation (10%), and testing (10%) sets. Geometric and photometric augmentation applies horizontal flipping, 45° rotation, color jittering, and random resized cropping, while DCGAN generates synthetic samples to balance minority classes. The EfficientNet-B1 model is fine-tuned using compound scaling, MBConv blocks, Swish activation, Batch Normalization, Cross-Entropy loss, and AdamW optimizer, with 5-fold cross-validation for robustness. Experimental results demonstrate that DCGAN-based augmentation achieves superior performance, with a test accuracy of 94% and an average F1-score of 0.93, outperforming geometric and photometric data augmentation (90% accuracy and 0.88 F1-score). DCGAN augmentation also significantly reduces misclassification between visually similar acne severity levels, particularly Level 0 and Level 1. These findings indicate that integrating DCGAN with EfficientNet-B1 effectively enhances generalization on imbalanced medical image datasets, providing a robust and replicable framework for acne severity classification and related medical imaging applications.]]>
<![CDATA[Integrated Maturity Assessment of Information Security for Land and Building Tax Management System Using National Institute of Standards and Technology Cybersecurity Framework 2.0, International Organization for Standardization/International Electrotechnical Commission 27002:2022, and Cybersecurity Capability Maturity Model 2.1. ]]>
<![CDATA[Paramesvari, Dhenok Prastyaningtyas]]>;
<![CDATA[Suseno, Jatmiko Endro]]>;
<![CDATA[Widodo, Catur Edi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5551
<![CDATA[Regional tax information systems such as the Sistem Informasi Manajemen Objek Pajak (SISMIOP) are vulnerable to cybersecurity threats due to the sensitivity of taxpayer data and the persistence of ad-hoc security management practices. These conditions pose risks to data confidentiality, integrity, and service availability, potentially undermining public trust and the effectiveness of local government services. This study aims to assess the information security maturity of SISMIOP operated by the Badan Pengelolaan Pendapatan, Keuangan, dan Aset Daerah (BPPKAD) through an integrated application of the NIST Cybersecurity Framework (CSF) 2.0, ISO/IEC 27002:2022, and the Cybersecurity Capability Maturity Model (C2M2) 2.1. A qualitative case study approach was employed. An organizational profile was developed using interviews, observations, and document analysis, followed by mapping 38 relevant NIST CSF subcategories to ISO/IEC 27002 controls and C2M2 capability domains. Security maturity was evaluated using questionnaires and interviews based on the C2M2 Maturity Indicator Levels (MIL0-MIL3), and a gap analysis was conducted against the target maturity level of MIL2. The results show that most cybersecurity functions, Govern, Identify, Detect, Respond, and Recover, remain at MIL1, indicating that practices are performed but not yet formalized or consistently implemented. The Protect function partially achieved MIL2. The largest gaps were identified in governance and risk management domains. Based on these findings, 38 prioritized strategic recommendations were formulated to improve policy formalization, risk management, technical controls, monitoring, and incident handling. This study contributes a practical and replicable multi-framework maturity assessment model to strengthen information security governance in public-sector tax information systems.]]>
<![CDATA[Balinese Statue Image Classification Using Transfer Learning: A Comparative Study of MobileNetV3 and EfficientNetV2 ]]>
<![CDATA[Wulandari, Dwi]]>;
<![CDATA[Iswara, Ida Bagus Ary Indra]]>;
<![CDATA[Yudi Antara, I Gede Made]]>;
<![CDATA[Putra Asana, I Made Dwi]]>;
<![CDATA[Gandika Supartha, I Kadek Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5556
<![CDATA[Balinese sculpture is an important form of cultural heritage that exhibits high visual diversity in terms of shape, structure, and carving style, which makes manual identification and documentation challenging. Previous studies on automated statue classification have generally focused on limited sculpture categories and therefore do not fully represent the visual diversity of Balinese sculptures. This study aims to develop an automatic image classification model capable of recognizing multiple Balinese statue categories using transfer learning and fine-tuning strategies. The proposed approach compares two convolutional neural network architectures, MobileNetV3 and EfficientNetV2, across eight statue classes: Dewa, Dewi, Mitologi, Penabuh, Pengapit, Punakawan, Raksasa, and Wanara. A dataset of 8,400 images was constructed from three-dimensional video documentation to capture multiple viewing angles of each statue. The images were processed through frame extraction, resizing, normalization, data augmentation, and dataset splitting. Model training was conducted in two stages, consisting of transfer learning followed by fine-tuning using reduced learning rates. Experimental results indicate that both models achieve high classification performance on the test dataset. MobileNetV3 obtained the highest test accuracy of 99.64% with a loss value of 0.0119, while EfficientNetV2 achieved an accuracy of 98.56% with a loss of 0.0613. These findings demonstrate that lightweight architectures can deliver competitive performance when supported by appropriate training strategies. This study provides a comparative evaluation of efficient deep learning models for cultural heritage image classification and supports the development of more reliable and systematic digital documentation of Balinese sculptures.]]>
