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
1,314 Documents
<![CDATA[Hybrid DeepLabV3+ With Embedded U-Net Skip Connections for Precise Brain Tumor Segmentation in MRI ]]>
<![CDATA[Abdul Karim]]>;
<![CDATA[Iwan Purnama]]>;
<![CDATA[Kusmanto Kusmanto]]>;
<![CDATA[Agus Perdana Windarto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5663
<![CDATA[Brain tumor segmentation from MRI is crucial for diagnosis, treatment planning, and patient monitoring, yet many automated segmentation models struggle to capture global contextual information while preserving fine spatial details, leading to inaccurate delineation of complex tumor boundaries and reduced clinical reliability. Manual segmentation by radiologists is also constrained by observer variability, prolonged analysis time, and reliance on individual expertise. To address these limitations, this study proposes Hybrid DeepLabV3+, an automated segmentation model that combines DeepLabV3+ multi-scale context modeling through Atrous Spatial Pyramid Pooling (ASPP) with spatial-detail preservation via U-Net skip connections. The objective is to develop and rigorously evaluate a more accurate and generalizable brain tumor segmentation model compared with U-Net, DeepLabV3+, and EfficientNet baselines. The model is trained on the BraTS dataset using T2-weighted MRI and evaluated using Dice score, intersection over union (IoU), precision, recall, and accuracy. Experimental results show that the proposed model achieves a Dice score of 0.785, IoU of 0.740, precision of 0.796, recall of 0.985, and accuracy of 0.991, surpassing the comparison models. These results indicate that Hybrid DeepLabV3+ can identify and map tumor regions with high fidelity, supporting its potential integration into clinical decision-support systems.]]>
<![CDATA[Enhancing Accuracy and Interpretability of Facial Skin Tone Classification Using Hybrid Feature Fusion and LightGBM ]]>
<![CDATA[Azed Yayah Durrotun Nihayah]]>;
<![CDATA[Joseflim Marchel]]>;
<![CDATA[Joseph Teguh Santoso]]>;
<![CDATA[Wiwid Wahyudi]]>;
<![CDATA[Noorul Hassan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5668
<![CDATA[Facial skin tone classification plays an important role in cosmetic recommendations and digital beauty applications. Existing approaches frequently rely on single-domain feature extraction combined with conventional classifiers, which may limit representational coverage and analytical transparency. This study proposes a hybrid feature-fusion framework that integrates HSV color descriptors, GLCM statistical texture features, and LBP micro-texture patterns into a unified feature representation, which is then classified using a Light Gradient Boosting Machine (LightGBM). Experiments were conducted on 899 facial images from the Kaggle Skin Tone dataset, categorized into White, Olive, and Dark Brown classes. A stratified 70:30 hold-out scheme was applied to evaluate model performance. The results indicate that combining heterogeneous visual descriptors improves classification accuracy compared to individual feature domains. Under the selected experimental configuration, the LightGBM classifier achieved an accuracy of 89.67%, with consistent precision and recall values across classes. In addition to overall performance metrics, balanced accuracy and confusion matrix analysis were employed to examine class-level prediction behavior. SHAP-based interpretability analysis was further utilized to identify feature contribution patterns, showing that chromatic descriptors played a prominent role while texture features contributed to boundary refinement. These findings suggest that hybrid feature integration combined with boosting-based modeling provides a structured and interpretable approach for moderate-scale skin tone classification tasks.]]>
<![CDATA[Handling Data Imbalance in Blog Page Classification Using SMOTE and ADASYN with Tree-Based Ensemble Models ]]>
<![CDATA[Siti Nuradilla]]>;
<![CDATA[Budi Susetyo]]>;
<![CDATA[Cici Suhaeni]]>;
<![CDATA[Fatchurrachman Fatchurrachman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5671
<![CDATA[SEO optimization plays a critical role in improving web page visibility in search engines. However, the limited proportion of Good SEO articles creates class imbalance, which biases classification models toward the majority class and reduces their ability to detect high-performing content. This study investigates effective strategies for handling data imbalance in SEO performance classification and evaluates the suitability of tree-based ensemble models for this task. Two oversampling techniques, the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), were applied to a dataset containing textual and numerical SEO features. Textual components were transformed using FastText embeddings to capture semantic relationships among on-page elements. Random Forest and XGBoost models were then trained and evaluated using balanced accuracy, precision, recall, and F1-score. The experimental results indicate that Random Forest combined with SMOTE achieved the highest balanced accuracy of 74.2%, outperforming other configurations in identifying Good SEO articles. Although XGBoost with ADASYN also achieved a competitive balanced accuracy of 74.0%, its performance remained slightly lower than Random Forest with SMOTE. These findings confirmed that appropriate imbalance handling significantly enhanced classification fairness and minority-class sensitivity. From a computer science perspective, this study advances research in imbalanced text classification and web mining by providing empirical evidence that integrating semantic embeddings with adaptive resampling strategies improves predictive robustness and model reliability in real-world digital content evaluation. The proposed framework supports the development of automated, data-driven SEO auditing systems. ]]>
<![CDATA[Agile-Based Development and System Usability Scale Evaluation of a Web-Based Daycare Information System for Parental Engagement ]]>
<![CDATA[Ni Made Rai Masita Dewi]]>;
<![CDATA[I Gusti Agung Ayu Sekarini]]>;
<![CDATA[Ni Made Ayu Juli Astari]]>;
<![CDATA[I Gede Putra Mas Yusadara]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5674
<![CDATA[The study employed an Agile-based system development approach supported by Unified Modeling Language (UML). Functional validation was conducted using black-box testing to ensure system conformity with user requirements. Usability evaluation was performed using the System Usability Scale (SUS) after the system had been used operationally for three months by 25 respondents representing various user roles. Parental engagement was measured using an adapted instrument contextualized to the use of a daycare information system. The results showed that the system achieved a SUS score of 82, which falls into the Good and acceptable category, while the mean parental engagement score reached 4.37, indicating a high level of engagement. These findings suggest that the system provides a good level of usability and is capable of supporting active parental participation in monitoring children’s development. The scientific contribution of this study lies in providing an integrated evaluation framework that combines Agile-based development, standardized usability measurement, and user engagement analysis within a real operational context. This study extends usability research beyond technical interface evaluation toward understanding user engagement within digital information service systems.]]>
<![CDATA[Enhancing PROMETHEE-Based Decision Support Systems Through Evolutionary Weight Optimization ]]>
<![CDATA[Dodi Siregar]]>;
<![CDATA[Yuyun Dwi Lestari]]>;
<![CDATA[Mesran Mesran]]>;
<![CDATA[Roznim Roznim]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5690
<![CDATA[Multi-Criteria Decision Making (MCDM) is a crucial issue in various strategic domains, including the energy and utilities sectors, involving multiple alternatives and criteria with varying degrees of importance. One widely used MCDM method is PROMETHEE, but its performance relies heavily on the determination of criteria weights, which are generally subjective and static. This study aims to improve the performance of a PROMETHEE-based decision support system through optimization of criteria weights. The research dataset is sourced from InfluxDB for Energy and Utilities and consists of 219 alternatives with a number of operational parameters represented in the form of a decision matrix. The proposed method integrates PROMETHEE with a weight optimization mechanism, where weights are treated as decision variables and PROMETHEE is used as an evaluation function in the optimization process. The performance of the proposed method is compared with the baseline PROMETHEE using indicators of net flow value, alternative ranking, ranking changes, and net flow variance. Experimental results show that weight optimization significantly improves the system's discrimination power, as indicated by an increase in net flow variance from 0.189 in the baseline PROMETHEE to 1.118 in the optimized PROMETHEE. Furthermore, weight optimization results in a more structured and adaptive ranking to data characteristics, without disrupting the stability of extreme alternatives. These findings confirm that criterion weight optimization is capable of improving the objectivity, reliability, and quality of decisions in a PROMETHEE-based decision support system, thus the proposed method has the potential to be applied to various complex multi-criteria decision-making problems. From the perspective of Informatics and Computer Science, this research contributes to the development of intelligent decision support systems by integrating computational optimization techniques with MCDM methods to support more adaptive and data-driven decision-making processes.]]>
<![CDATA[Operational Diagnostics for Intrusion Detection: SHAP-Guided Failure Casebook and SOC Triage Rationale with XGBoost and RandomForest ]]>
<![CDATA[Fathoni Mahardika]]>;
<![CDATA[Ema Utami]]>;
<![CDATA[Kusrini Kusrini]]>;
<![CDATA[Ferry Wahyu Wibowo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5711
<![CDATA[Operational deployment of machine-learning (ML) intrusion detection systems (IDS) in security operations centers (SOC) often generates large volumes of alerts that are difficult to justify, prioritize, and audit. Although explainable AI (XAI) techniques are increasingly used to interpret IDS predictions, most studies remain limited to post-hoc explanations and do not translate insights into analyst-facing operational diagnostics. This paper proposes Explainability as Operational Diagnostics, a SHAP-guided workflow that operationalizes explanations into two reusable artifacts: (1) a Failure Casebook that captures recurring false-positive and false-negative patterns using explanation signatures and confidence cues, and (2) a Triage Rationale that maps attribution evidence and prediction confidence into transparent investigation priorities for SOC analysts. The workflow is evaluated using flow-based IDS data with 78 features and two supervised classifiers, XGBoost and Random Forest, applied to an explainability subset of 1,000 instances per model. In the analyzed baseline run, both models achieve perfect separation (TN = 639, TP = 361, FP = 0, FN = 0), resulting in an empty failure casebook under this controlled condition. Rather than presenting this outcome as a final operational claim, we frame it as baseline diagnostic validation and explain how the workflow would populate the casebook under realistic conditions, including class imbalance, noise, and distribution shift. Even without misclassifications, SHAP analyses reveal consistent dominant flow-level drivers across models, providing a reproducible foundation for SOC justification and model auditing. Overall, this work advances XAI for cybersecurity from prediction-centric interpretation toward actionable operational diagnostics that support trustworthy ML deployment in high-risk SOC environments.]]>
<![CDATA[A Systematic Review and Meta-Analysis of Deep Learning Architectures, Preprocessing Techniques, and Datasets for Oil Palm Disease Classification ]]>
<![CDATA[Hadiguna Setiawan]]>;
<![CDATA[Ali Ibrahim]]>;
<![CDATA[Mariana Purba]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5721
<![CDATA[Oil palm disease detection remains a significant challenge in precision agriculture due to variability in dataset quality, image acquisition conditions, and deep learning model performance. This study presents a systematic review and meta-analysis of deep learning architectures, preprocessing techniques, and dataset characteristics used in oil palm disease classification. The review followed the PRISMA framework, identifying 165 articles from IEEE Xplore, SpringerLink, and ScienceDirect. After applying eligibility and quality criteria, 11 studies were included for qualitative and quantitative synthesis. The analyzed methods include Convolutional Neural Networks (CNN), ResNet, EfficientNet, U-Net, Artificial Neural Networks (ANN), and hybrid models integrating preprocessing techniques such as CLAHE and Median Blur. Reported accuracy ranged from 83.21% to 99.67%. Meta-analytical findings indicate that dataset diversity, class balance, and image enhancement significantly influence performance, with preprocessing contributing to accuracy improvements of up to approximately 5%. This study provides the first focused synthesis on oil palm disease classification. It highlights the importance of standardized datasets and robust methodological design to enhance reproducibility and support precision agriculture informatics.]]>
<![CDATA[A Systematic Literature Review of Deep Learning-Based Non-Organic Waste Classification: Performance Analysis, Dataset Characteristics, and Research Gap Identification ]]>
<![CDATA[Erwin Dwika Putra]]>;
<![CDATA[Marissa Utami]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5728
<![CDATA[The rapid growth of non-organic waste has created a demand for smart automated classification systems to improve waste management. Although deep learning has been widely used, differences in model designs, datasets, and evaluation methods in earlier research have led to inconsistent conclusions about real-world effectiveness. This review analyzes 40 peer-reviewed journals focused on deep learning for classifying non-organic waste. It examines model types, dataset usage, and evaluation metrics to identify research trends and gaps. Results show that Convolutional Neural Networks (CNNs), especially deep architectures like Residual and Efficient Networks, achieve higher accuracy on standard benchmarks. Yet, these models often perform less well with actual waste data, revealing limitations in generalization. Accuracy remains the main metric, while metrics like precision, recall, and F1-score are less consistently reported. Findings highlight that classification success depends more on dataset realism and evaluation design than on model complexity alone. This study offers a structured synthesis to identify research gaps and guide the development of more reliable, practical waste classification systems. From an informatics point of view, it provides a framework for performance evaluation that supports evidence-based model selection and the creation of robust, deployable intelligent classifiers.]]>
<![CDATA[Daily Rainfall Prediction and Intensity Classification in Sleman Regency Using Random Forest Regressor ]]>
<![CDATA[Halim Bagus Al Hakim]]>;
<![CDATA[Nasy'an Taufiq Al Ghifari]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5730
<![CDATA[Daily rainfall forecasting is essential for water resource management, agricultural planning, and hydrometeorological disaster mitigation, especially in tropical regions such as Indonesia that experience high climate variability. This study aims to develop a daily rainfall prediction model for Sleman Regency using a Random Forest Regressor (RFR) involving 11 input features, consisting of eight surface meteorological variables and three lag variables based on daily data from 2015 to 2025. This model is designed to capture non-linear relationships and temporal dependencies on rainfall patterns. In the test data, the model achieved a Mean Absolute Error (MAE) of 7.82 mm, a Root Mean Squared Error (RMSE) of 15.34 mm, and an R² of 0.396, which explains about 39.6% of the variation in daily rainfall. To improve the interpretation of the prediction results, the output of this model is classified into the category of rain intensity in accordance with BMKG standards. The results showed that the Random Forest Regressor was effective for modeling general rainfall trends and temporal patterns, but difficulties in predicting extreme rainfall events, mainly due to class imbalances and ensemble averages. This study provides a practical framework for combining non-linear regression with operational rainfall intensity classification using long-term daily data, advancing computational approaches for environmental intelligence in Informatics.]]>
<![CDATA[Integrated Sales and Business Management System Development for Culinary SMEs using Design Science Research and Usability Evaluation ]]>
<![CDATA[I Gusti Agung Ayu Sekarini]]>;
<![CDATA[Anak Agung Sandatya Widhiyanti]]>;
<![CDATA[Ni Made Rai Masita Dewi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 5 (2026): JUTIF Volume 7, Number 5, October 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.5.5735
<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector often face challenges in managing sales transactions and business data due to fragmented and manual operational processes. This condition hinders data integration and information transparency, which are essential for effective business monitoring. This study aims to develop an Integrated Sales and Business Management System (ISBMS) as an integrated information system that unifies sales operations and business management within a single platform. This research adopts the Design Science Research (DSR) methodology by implementing a layered architecture approach to ensure system modularity and scalability. The developed system integrates the functions of a Transaction Processing System (TPS), Management Information System (MIS), and Decision Support System (DSS) at the descriptive analytics level, along with a digital product catalog module as part of the e-commerce functionality. Usability evaluation was conducted using the System Usability Scale (SUS) involving 10 respondents consisting of business owners and operational staff. The results indicate that ISBMS achieved an average SUS score of 80.4, which falls within the “Good” to “Excellent” category with an “Acceptable” level of acceptability. These findings demonstrate that an integrated system approach can improve data consistency and business information visibility in a more structured manner, while also contributing to the development of an information system architecture model that is relevant to MSME operational needs.]]>
