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[Automated Facial Wrinkle Segmentation for Dermatological Assessment Using VGG-Based U-Net with Hybrid Augmentation ]]>
<![CDATA[Setiawan, Wahyu Fajar]]>;
<![CDATA[Suciati, Nanik]]>
<![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.2026.7.2.5561
<![CDATA[Manual and automated facial wrinkle segmentation remains challenging due to the fine-grained nature of wrinkles, uneven distribution across facial regions, severe class imbalance (~2% wrinkle pixels), and sensitivity to lighting variations—limiting the reliability of existing dermatological assessment tools. This study aims to evaluate VGG transfer learning with hybrid augmentation strategies for U-Net-based automated facial wrinkle segmentation. Using the FFHQ-Wrinkle dataset comprising 1,000 manually annotated high-resolution images (1024×1024 pixels), this study systematically evaluates three U-Net variants (Baseline, VGG16-based, VGG19-based) across four augmentation strategies: no augmentation, hierarchical image enhancement (CLAHE, gamma correction, bilateral filtering), geometric transformation (rotation, translation, shear, zoom, flip), and hybrid combination. A multi-component loss function integrating Focal Loss, Dice Loss, IoU Loss, and Boundary Loss addresses class imbalance while optimizing both region overlap and edge localization. The proposed VGG19-based U-Net with hybrid augmentation achieves state-of-the-art performance: Dice coefficient of 0.6585, IoU of 0.4970, precision of 0.6186, recall of 0.7344, and Boundary F1 of 0.9185. Key findings demonstrate that VGG19 transfer learning provides +21.54% Dice improvement over Baseline U-Net with 12.7-fold reduction in overfitting, while hybrid augmentation yields +4.87% Dice improvement with +2.24% synergistic gain beyond individual strategies. This research advances automated dermatological tools for precise skin health assessment, reducing subjectivity in clinical evaluations and providing actionable guidelines for practitioners developing automated wrinkle analysis systems. ]]>
<![CDATA[Implementation of Agile Method and Apriori Algorithm for Recommendation System in Outdoor Equipment Rental Services ]]>
<![CDATA[Elfreda, Raditya Prama]]>;
<![CDATA[Usman, Muhammad Lulu Latif]]>
<![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.2026.7.2.5567
<![CDATA[Drop Outdoor Purwokerto faces inefficiencies in its outdoor equipment rental process, where customers are required to visit the store directly to check item availability, often resulting in miscommunication and suboptimal transaction management. To address this issue, this study aims to design and develop a web-based outdoor equipment rental information system that enables real-time availability checking and efficient online booking. The system is developed using the Agile methodology to accommodate dynamic user requirements and iterative system improvements. In addition, the Apriori algorithm is implemented to analyze historical rental transaction data and generate item recommendations based on association rule mining. The analysis results indicate that several outdoor equipment items exhibit strong association patterns, with the highest lift value exceeding 1, signifying meaningful relationships beyond random co-occurrence. These patterns are utilized as the basis for the recommendation feature within the system. Functional testing using Black Box Testing shows that all system features operate as expected, achieving a 100% success rate across tested scenarios, including transaction processing, cart management, and recommendation display. The findings demonstrate that integrating the Agile development approach with Apriori-based data mining can effectively support data-driven decision-making in outdoor equipment rental services. This study contributes to the development of recommendation systems for small and medium-sized rental businesses by highlighting the practical application of association rule mining on rental transaction data, which exhibits characteristics distinct from conventional retail datasets.]]>
<![CDATA[Forensic Evaluation of the Effectiveness of Private Browsing Modes in Google Chrome and Mozilla Firefox Using the National Institute of Standards and Technology Framework Integrated with Artificial Intelligence ]]>
<![CDATA[Syukri, Muhammad]]>;
<![CDATA[Riswaya, Asep Ririh]]>;
<![CDATA[Budiman, Dheni Apriantsani]]>
<![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.2026.7.2.5577
<![CDATA[As cyber threats and the misuse of personal data continue to increase, private browsing modes in web browsers such as Google Chrome and Mozilla Firefox are often perceived as solutions to enhance user privacy. However, these modes still leave traces of sensitive data in volatile memory (RAM), even though artifacts stored on disk-based storage are removed. This study evaluates the effectiveness of private browsing modes using the National Institute of Standards and Technology (NIST) framework integrated with Artificial Intelligence (AI) for forensic analysis. Simulation scenarios were conducted to assess the ability of private browsing modes to prevent data retention. The results indicate that although private browsing modes successfully eliminate disk-based traces, sensitive data such as account credentials can still be extracted from RAM. The integration of AI accelerates the detection of these artifacts. This research contributes to the field of digital forensics by providing a systematic framework for evaluating browser privacy mechanisms and offering insights for the development of real-time browser security tools.]]>
<![CDATA[Cybersecurity Risk Detection Based on Roblox User Review Analysis Using TF-IDF and Comparison of Naïve Bayes and Support Vector Machine ]]>
<![CDATA[Alam, RG Guntur]]>;
<![CDATA[Ibrahim, Huda]]>
<![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.2026.7.2.5582
<![CDATA[The rapid growth of online gaming platforms increases user engagement while also exposing users to technical and cybersecurity risks. User reviews represent a rich yet underutilized textual source that can serve as early indicators of such risks. Unlike prior studies focused on sentiment polarity, this study positions user reviews as early cybersecurity risk signals by mapping complaint patterns into operational security risk categories relevant to system developers. This study compares Naïve Bayes (NB) and Support Vector Machine (SVM) in detecting cybersecurity risks from imbalanced textual data derived from Roblox user reviews. A total of 3,000 reviews were collected from the Google Play Store via web scraping and preprocessed using case folding, normalization, tokenization, stopword removal, and stemming. Reviews were classified into four cybersecurity risk categories (account access issues, suspicious behavior, connection instability, and data loss) based on rule-based security keyword mapping. Text representation employed TF-IDF with unigram and bigram features, while class imbalance was handled through undersampling. Model evaluation used three train–test splits (80:20, 70:30, and 60:40) and was assessed using Accuracy, Macro F1-score, AUC-PR, training time, and statistical testing. Results show that SVM consistently outperforms Naïve Bayes, achieving higher accuracy (0.86–0.88) and substantially better Macro F1-scores (0.73–0.77), indicating more balanced detection of minority cybersecurity risks. These differences are statistically significant (p < 0.05). The novelty of this study lies in transforming user reviews into a structured cybersecurity risk detection framework and empirically demonstrating the robustness of SVM in identifying rare but critical risks from imbalanced data.]]>
<![CDATA[User Acceptance Analysis of SINAGA Digital Attendance System Using Integrated UTAUT and SCT Models with PLS-SEM for Civil Servants in Purbalingga Regency ]]>
<![CDATA[Latif, Imam Sofarudin]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Barkah, Azhari Shouni]]>
<![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.2026.7.2.5584
<![CDATA[This study combines two main theories, namely the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), to analyze the level of user acceptance of the SINAGA digital attendance system among civil servants in Purbalingga Regency. This study aims to identify factors that influence technology adoption through an integrated UTAUT approach with SCT moderation, particularly self-efficacy. The method used was a survey of 102 respondents, with analysis using Partial Least Squares-Structural Equation Modeling (PLS-SEM) involving testing of outer and inner models through the Slovin approach. The results show that factors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) significantly influence Behavioral Intention (BI). Self-Efficacy (SE) and Outcome Expectancy (OE) also act as moderating factors that strengthen the relationship between PE and BI, as well as EE and BI. With an R2 value of 78%, this model has a high explanatory power regarding users' behavioral intentions in adopting the system. This study contributes to the development of technology acceptance theory in the public sector, particularly for e-government systems, and suggests improving users' digital competence and optimizing infrastructure to support further technology acceptance with the integration of artificial intelligence (AI) technology in the system for more efficient dynamic monitoring. The main contribution of this research is the development of digital systems within the Indonesian government, in line with the sustainability of technology adoption in the public sector.]]>
<![CDATA[Expert System for Diagnosing Autoimmune Diseases Using Dempster–Shafer and Fuzzy Logic: A Case Study of Prof. Dr. Margono Soekarjo Regional Hospital ]]>
<![CDATA[Rahmadani, Ragil Putri]]>;
<![CDATA[Nur, Yohani Setiya Rafika]]>;
<![CDATA[Utami, Annisaa]]>
<![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.2026.7.2.5585
<![CDATA[Autoimmune diseases, particularly lupus, pose a major challenge in healthcare because their symptoms are highly variable and often mimic other medical conditions. Delayed diagnosis can worsen patient outcomes, increase the risk of severe complications, and even lead to death, especially in healthcare facilities with limited autoimmune subspecialists, such as Prof. Dr. Margono Soekarjo Regional Hospital. This study aims to develop a web-based expert system to support early screening for lupus by combining the Fuzzy Tsukamoto method and the Dempster-Shafer theory. The Fuzzy Tsukamoto method is used to represent symptom uncertainty through fuzzification, while the Dempster-Shafer theory is used to combine evidence from individual symptoms to produce confidence levels for possible diagnoses. The research process included a literature review, expert interviews, construction of a symptom–disease knowledge base, design of fuzzy rules, implementation of mass function calculations, and development of a web-based diagnostic application. Testing was conducted using ten patient test cases with confirmed expert diagnoses. The test results showed an accuracy of 100%, with all system diagnoses matching the experts’ diagnoses. The strength of this research lies in the integration of two inference methods to improve the accuracy of evidence calculation, and in the use of symptom uniqueness and occurrence parameters that were validated directly by experts. This system has the potential to serve as an effective early screening tool for healthcare providers and patients, particularly in resource-limited settings. From an informatics perspective, this study contributes to the development of intelligent decision support systems by demonstrating the effectiveness of a hybrid reasoning approach in handling uncertainty in medical diagnosis. The integration of Fuzzy Tsukamoto and Dempster–Shafer methods enhances diagnostic consistency and reliability, making the proposed system relevant for research in expert systems and medical informatics.]]>
<![CDATA[Cloud Computing-Based U-Net Integration for Post-Landslide Satellite Image Segmentation ]]>
<![CDATA[Pratiwi , Swelandiah Endah]]>;
<![CDATA[Asnur, Paranita]]>;
<![CDATA[Fitrianingsih, Fitrianingsih]]>;
<![CDATA[Senjaya, Remi]]>;
<![CDATA[Nurdin, Muhammad Sahal]]>
<![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.2026.7.2.5617
<![CDATA[Landslides are geological disasters that cause severe impacts on human life, infrastructure, and ecosystems, highlighting the need for post-disaster mapping methods that are fast, accurate, and scalable. This study aims to develop a post-landslide satellite image segmentation framework based on U-Net integrated with cloud computing to support large-scale and operational disaster mapping. While U-Net has been widely applied for landslide analysis, most existing studies focus on local-scale assessments or susceptibility mapping and lack integration with cloud-based pipelines and multi-source data for post-disaster operations. The novelty of this research lies not in modifying the U-Net architecture, but in integrating multi-source geospatial data, system workflow, and scalable cloud deployment. The proposed framework utilises a global multi-source dataset consisting of RGB imagery, Normalized Difference Vegetation Index (NDVI), slope, and elevation to enhance robustness and generalisation across diverse geomorphological conditions. Experimental results show stable model convergence with a final loss of 0.0357, an F1-score exceeding 0.75, and an AUC-PR of 0.8391. Evaluation on the testing dataset achieves a precision of 0.7692, recall of 0.7519, F1-score of 0.7604, and Intersection over Union of 0.6135. Qualitative analysis demonstrates strong spatial agreement between predicted segmentation and ground truth, with minor deviations mainly along complex slope boundaries. From an Informatics perspective, this study contributes by operationalizing deep learning through cloud computing to enable scalable computation, parallel processing, and system-level deployment, while providing object-level estimates of landslide events and affected areas to support disaster response and risk mitigation.]]>
<![CDATA[Aggregation Model to Determine Criteria Weights for Integrated Primary Health Care Information System (IPCIS) Implementation ]]>
<![CDATA[Kusumadewi, Sri]]>;
<![CDATA[Kurniawan, Rahadian]]>;
<![CDATA[Wahyuni, Elyza Gustri]]>;
<![CDATA[Arifin, Aridhanyati]]>;
<![CDATA[Rosita, Linda]]>;
<![CDATA[Mutmainna, Mutmainna]]>
<![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.2026.7.2.5619
<![CDATA[Implementation of the Integrated Primary Health Care Information System (IPCIS) in integrated community health posts (posyandu) is influenced by various factors, including technical aspects, human resources, policies, and data governance. Given the diverse field conditions, the impact of each factor can vary, so it is important to understand the relative importance of each criterion. This study aims to determine the weight of the criteria that influence the implementation of IPCIS in posyandu. Ten people answered the questions correctly (out of 22 respondents), including cadres, sub-district staff, and health workers from Tirtorahayu Village. Respondent preferences were collected using three approaches: rank-based aggregation (Borda, Condorcet, Copeland), score-based aggregation (average), and voting-based aggregation (plurality and majority) to obtain the criteria weights (w) and a comparative analysis between the approaches. The findings demonstrate that the IPCIS criteria for security and protection of personal data were consistently given the highest weights. In the ranking-based aggregation approaches (w_Borda=0.11, w_Condorcet=0.20, w_Copeland=0.19). In score-based aggregation approaches (w=0.11). In voting-based aggregation approaches (w=0.15). It is indicating a strong group consensus regarding the importance of these aspects in IPCIS implementation. The combination of ranking-based and score-based aggregation resulted in stable IPCIS implementation criterion weights that reflected group consensus, with voting-based aggregation acting as validation. The practical implication is that the obtained weighted criteria can be used as a basis for determining program priorities and resource allocation when implementing IPCIS.]]>
<![CDATA[Long Short Term Memory and Gradient Boosting Model for One Day Ahead Forecasting of ANTAM Gold Bar Prices ]]>
<![CDATA[Ashari, Annisa]]>;
<![CDATA[Situmorang, Zakarias]]>;
<![CDATA[Rosnelly, Rika]]>
<![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.2026.7.2.5630
<![CDATA[This study develops and optimizes a hybrid LSTM-XGBoost forecasting model for daily ANTAM gold bar prices. The model utilizes historical time-series data of ANTAM gold prices, enriched with macroeconomic variables including the USD/IDR exchange rate and Brent oil prices, as well as derived features such as returns, lags, rolling statistics, and calendar effects. The LSTM component captures medium-term sequential patterns from the price series and macroeconomic variables, while the XGBoost component exploits a rich set of tabular features to model nonlinear relationships and volatility dynamics. Both models are trained and tuned separately, then combined through a weighted ensemble scheme in which the optimal weight is selected by minimizing Mean Absolute Percentage Error (MAPE) on the validation set. Experimental results on the test set show that the proposed hybrid model achieves Mean Squared Error (MSE) of 26,891,172.36, Root Mean Squared Error (RMSE) of 16,398.53, MAPE of 0.0058 (approximately 99.42% accuracy), and coefficient of determination \mathbit{R}^\mathbf{2} of 0.9971, outperforming a naïve baseline that assumes “tomorrow’s price equals today’s price”. The optimized LSTM-XGBoost hybrid model proves highly effective for short-term ANTAM gold price forecasting, providing reliable decision support for Indonesian gold market stakeholders.]]>
<![CDATA[Adaptive Gradient Boosting for Fuel Consumption Prediction in Mining Haul Trucks under Concept Drift Monitoring ]]>
<![CDATA[Kusnawi, Kusnawi]]>;
<![CDATA[Wibowo , Mochamad Agung]]>;
<![CDATA[Sanjaya, Ridwan]]>
<![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.2026.7.2.5635
<![CDATA[Fuel consumption prediction models deployed in mining operations often degrade in performance due to changes in the distribution of high-frequency telemetry data, a phenomenon commonly associated with concept drift. Static machine learning models trained on historical data may therefore lose reliability over time in dynamic operational environments. This study aims to develop an adaptive regression approach for predicting fuel consumption in mining haul trucks by integrating a Gradient Boosting Regressor with batch-wise performance monitoring and periodic retraining. Real-world telematics data were processed through systematic preprocessing and feature engineering to derive behavioral and operational indicators relevant to fuel usage. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²), while drift monitoring employed a threshold-based MAE analysis over streaming batches. Experimental results show that the initial model achieved an MAE of 27.27 L/h and an R² of 0.759, and the adaptive retraining strategy provided marginal yet consistent performance stabilization without detecting significant drift within the observed period. Beyond the mining application, this framework contributes to the development of lightweight adaptive regression systems for real-time data stream processing, supporting computationally efficient predictive maintenance in industrial IoT environments.]]>
