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
1,242 Documents
<![CDATA[Usability Evaluation Methods and Their Contribution to UI/UX Design in e-Government: A Systematic Literature Review ]]>
<![CDATA[Sopian Amir]]>;
<![CDATA[Harry Budi Santoso]]>;
<![CDATA[Baginda Anggun Nan Cenka]]>;
<![CDATA[Nandhita Zefania Maharani]]>;
<![CDATA[Shabrina Salsabila Kurniawan]]>;
<![CDATA[Lina Fitria]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5587
<![CDATA[Indonesia's digital government transformation has increased efficiency and transparency. However, usability issues continue to hinder the effectiveness of e-government services. These challenges highlight the need for appropriate usability evaluation methods to inform evidence-based UI/UX design improvements. This study conducted a systematic literature review following the PRISMA 2020 guidelines. The review examined usability evaluation methods applied to e-government applications and analysed how the identified usability issues translate into UI/UX design recommendations. Articles were selected based on defined inclusion and exclusion criteria, focusing on empirical studies published between 2020 and 2025. A total of 22 studies were included in the final synthesis. The findings demonstrate a strong reliance on a multi-method usability evaluation approach, specifically a combination of System Usability Scale (SUS), User Experience Questionnaire (UEQ), with usability testing, think-aloud, heuristic evaluation, and eye tracking. Common usability issues include unclear visual hierarchies, complex navigation structures, inadequate feedback mechanisms, and high cognitive load. These issues were systematically synthesized based on usability goals, providing structured insights into recurring design improvement patterns. This study provides an analytical synthesis connecting usability evaluation issues across multiple methods with UI/UX design recommendations, extending previous studies that primarily focused on method identification. The findings offer evidence-based guidance for designers and policymakers to improve the usability of e-government services. However, the study has limitations due to its geographic concentration. Future research is needed to evaluate internal government applications and conduct longitudinal studies assessing the impact of UI/UX improvements on the adoption of public services.]]>
<![CDATA[Fairness Analysis of Random Forest and Support Vector Machine Models for Predicting Student Academic Performance Based on Demographic and Digital Activities ]]>
<![CDATA[Hamdatul Mabruroh]]>;
<![CDATA[Rayhan Rizky Widi Ananta]]>;
<![CDATA[Vinna Rahmayanti Setyaning Nastiti]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5590
<![CDATA[The application of machine learning models in digital education has become increasingly important for predicting student academic performance and supporting early academic interventions. However, predictive accuracy alone is insufficient if models introduce bias against certain demographic groups. This study presents a fairness analysis of student academic performance prediction using Random Forest and Support Vector Machine based on demographic attributes and digital learning activities. The Open University Learning Analytics Dataset (OULAD) comprising 32,593 student records was utilized, with academic outcomes reformulated as a binary classification problem (pass/fail). Model performance was evaluated using accuracy, precision, recall, and F1-score, while fairness was assessed using Demographic Parity and Equal Opportunity metrics on the sensitive attribute highest prior education level. Experimental results show that Random Forest achieved superior predictive performance (F1-score = 0.835) compared to SVM (F1-score = 0.746). From a fairness perspective, Random Forest demonstrated lower disparity, reducing demographic parity gap from 0.466 (SVM) to 0.380, and equal opportunity disparity from 0.391 to 0.199 across education-level groups., indicating an overall bias reduction of approximately 15–20% across education-level groups. The findings highlight that models with higher predictive accuracy do not necessarily ensure fairness across demographic groups. The novelty of this study lies in the integrated evaluation of demographic fairness and digital learning activities within the OULAD context, extending prior studies that focus primarily on performance optimization. This research underscores the importance of incorporating fairness-aware evaluation in educational predictive systems to support ethical, transparent, and equitable decision-making in digital education.]]>
<![CDATA[Analysis and Comparative Prediction of Daily ET₀ Using FAO Penman-Monteith Integrated with LSTM and BiLSTM Models in Pinrang Regency ]]>
<![CDATA[Hanum Zalsabilah Idham]]>;
<![CDATA[Firji Achmad Fahresi]]>;
<![CDATA[Firdaus Firdaus]]>;
<![CDATA[Andi Akram Nur Risal]]>;
<![CDATA[Dewi Fatmarani Surianto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5602
<![CDATA[Accurate and adaptive irrigation water management is essential for maintaining rice productivity in regions with high climatic variability, such as Pinrang Regency, Indonesia. Conventional irrigation practices based on fixed schedules often fail to respond daily weather dynamics, resulting in inefficient water use. This study analyzes reference evapotranspiration (ET₀) as a climate-based indicator of irrigation water requirements and predicts daily ET₀ values using deep learning approaches. ET₀ was calculated using the FAO Penman-Monteith method based on daily meteorological variables, including air temperature, relative humidity, wind speed, surface pressure, and solar radiation. Daily climate data for the 2018-2024 period were obtained from the NASA POWER database, comprising 2,892 observations, and processed using Min-Max normalization, a 30-day sliding window scheme, and chronological data partitioning. Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models were trained using the Adam optimizer to perform daily ET₀ prediction. The results show that ET₀ exhibits a distinct seasonal pattern, with higher values during the late dry season and lower values in the rainy season. Model evaluation indicates that BiLSTM slightly outperforms the LSTM model, achieving an MAE of 0.4185 mm/day, an RMSE of 0.5369 mm/day, and a MAPE of 10.42%, compared to the LSTM model with an MAE of 0.4205 mm/day, an RMSE of 0.5389 mm/day, and a MAPE of 10.44%. Medium-term ET₀ projections for the 2025-2027 period demonstrate consistent seasonal patterns relevant for irrigation planning. This study contributes to informatics by providing a reproducible deep learning-based time-series prediction framework to support adaptive irrigation management and sustainable agriculture.]]>
<![CDATA[Development of ESP32-Driven IoT System for Automatic Regulation of Temperature and Humidity in Melon Greenhouse Cultivation ]]>
<![CDATA[Miftakhul Maulidina]]>;
<![CDATA[Sofyan Ahmadi]]>;
<![CDATA[M. Dewi Manikta Puspitasari]]>;
<![CDATA[Mohamad Aditya]]>;
<![CDATA[Muhammad Nur Habib Maulidan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5608
<![CDATA[This research holds high urgency, especially in the context of modern agriculture facing various challenges. In reality, the use of water and energy in conventional farming is often inefficient. Manual watering does not always align with the actual needs of the plants and causes water wastage. The same goes for the use of non-automated fans. This causes temperature instability, which should ideally be between 25-30°C, and soil moisture, which should ideally be 60-80%. This can hinder melon growth. The objective of this study is to create an IoT prototype using ESP32, DHT22, soil moisture sensor, and relay for automatic pump/fan control. Through the Borg and Gall research development method with Telegram monitoring, the result achieved melon growth of 130.5 cm with the ESP32 IoT system. Meanwhile, the melon growth without using the ESP32 IoT system was 82.5 cm. This research contributes to informatics through a low-cost adaptive system with potential for Machine Learning integration to predict water needs and disease risks in precision farming.]]>
<![CDATA[A Performance Trade-Off Analysis Between Minutiae-Based Algorithm and Convolutional Neural Networks in Fingerprint Image Identification ]]>
<![CDATA[Raden Bagus Bambang Sumantri]]>;
<![CDATA[Fajar Mahardika]]>;
<![CDATA[Dede Yusuf]]>;
<![CDATA[Tri Stiyo Famuji]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5609
<![CDATA[Fingerprint image identification plays a crucial role in biometric authentication systems; however, selecting an appropriate algorithm remains challenging due to trade-offs between identification accuracy and computational efficiency. This study aims to comparatively evaluate the performance of a traditional Minutiae-Based algorithm and a Convolutional Neural Network (CNN) for fingerprint image identification to determine their respective strengths and limitations. The Minutiae-Based method extracts distinctive ridge features, such as ridge endings and bifurcations, followed by a similarity-based matching process. In contrast, the CNN model automatically learns discriminative features from raw fingerprint images through deep learning. Experiments were conducted on a multi-subject fingerprint dataset, and performance was assessed using identification accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and computation time per image. The results show that CNN achieved a higher identification accuracy of 98.6%, with a FAR of 1.2% and FRR of 1.4%, outperforming the Minutiae-Based algorithm, which obtained 92.3% accuracy, 4.8% FAR, and 3.9% FRR. However, the Minutiae-Based approach demonstrated superior computational efficiency, requiring an average processing time of 0.42 seconds per image compared to 1.35 seconds for CNN. These findings highlight a clear performance trade-off between accuracy and processing speed. The novelty of this study lies in providing a structured quantitative comparison that integrates accuracy, security metrics, and computational cost within a unified evaluation framework. The results contribute to the development of biometric systems by offering practical guidance for selecting fingerprint identification algorithms based on application-specific requirements, whether prioritizing high recognition accuracy or real-time computational efficiency.]]>
<![CDATA[Retrieval-Augmented Large Language Model Using FAISS for Stock Recommendation on LQ45 Index Based on Technical and Fundamental Analysis ]]>
<![CDATA[Murtiyoso Murtiyoso]]>;
<![CDATA[Imam Tahyudin]]>;
<![CDATA[Berlilana Berlilana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5610
<![CDATA[The analysis of Indonesian stock markets is inherently complex due to market volatility, heterogeneous financial indicators, and the need to integrate both technical and fundamental information. Traditional analytical approaches often struggle to provide transparent and consistent investment recommendations, while Large Language Models (LLMs) may suffer from hallucination when applied to structured financial prediction tasks. This study proposes a Retrieval-Augmented Generation (RAG) framework based on the LLaMA model and FAISS to enhance the reliability of stock investment recommendations for LQ45-listed companies. Historical OHLCV data and fundamental financial reports are collected from Yahoo Finance and preprocessed into a structured knowledge base using embedding representations. Relevant information is retrieved using a top-k similarity search mechanism and integrated into a structured prompt engineering scheme to generate BUY, HOLD, or SELL recommendations. Experimental results demonstrate that the proposed approach achieves an F1-score of 0.76 for classification performance, with regression-based error metrics of MAE 299.38 and MAPE 10.06%. In addition, text-based evaluation using ROUGE yields a high score of 0.9906, indicating strong alignment between generated explanations and reference analyses. The findings suggest that the RAG framework significantly reduces hallucination by grounding LLM outputs in retrieved financial data, thereby improving the interpretability and robustness of AI-driven stock analysis. This research contributes to the field of informatics by providing a practical and extensible RAG-based framework for structured financial prediction tasks.]]>
<![CDATA[Building Early-Warning Models for Multiclass Credit Risk Staging: A Comparative Study of Random Forest and XGBoost with SMOTENC and Borderline-SMOTE ]]>
<![CDATA[Gupita Nurmalita Sari]]>;
<![CDATA[Rujianto Eko Saputro]]>;
<![CDATA[Giat Karyono]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5611
<![CDATA[This paper develops and evaluates an early-warning credit risk classifier using a three-class formulation that distinguishes performing loans (L), delinquent accounts (DP), and non-performing loans (NPL). Early-warning modeling is challenging because deterioration events are relatively infrequent, yielding class imbalance and asymmetric error costs where missed NPL cases are particularly consequential. The study aims to build and compare operationally feasible models that improve NPL identification while maintaining reliable performance across all classes. Experiments use 2,253 loan records with 20 early-warning predictors and compare Random Forest and XGBoost under four imbalance-handling strategies: a no-resampling baseline, class/sample weighting, SMOTENC, and Borderline-SMOTE. Performance is estimated via stratified 5-fold cross-validation, with out-of-fold predictions aggregated for consistent fold-robust evaluation and probability-based diagnostics. Evaluation emphasizes imbalance-aware criteria, including macro F1, balanced accuracy, NPL precision/recall/F1, and ranking metrics for NPL versus non-NPL (PR-AUC and ROC-AUC), complemented by agreement statistics (Cohen’s kappa and Matthews correlation coefficient). Results show that class-sensitive metrics reveal clearer differences among strategies than global measures, with XGBoost combined with SMOTENC achieving the strongest NPL-oriented performance (recall 0.7389; F1 0.8035; PR-AUC 0.8780; ROC-AUC 0.9434). The contribution is a controlled out-of-fold benchmark of imbalance strategies for mixed-type tabular credit staging, providing a reproducible evaluation protocol for financial informatics and imbalanced multiclass learning.]]>
<![CDATA[Machine Learning Approaches with Random Forest and XGBoost for Sustainable Tourism Forecasting in Bali Destinations ]]>
<![CDATA[Nadia Nabila]]>;
<![CDATA[Yohani Setiya Rafika Nur]]>;
<![CDATA[Maie Istighosah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5612
<![CDATA[Bali has experienced rapid tourism growth, reaching more than 6.3 million international visitors in 2024, which has increased the risk of overtourism and created challenges for sustainable destination management. Despite tourism being a major contributor to Bali’s economy, planning practices have not fully adopted data-driven prediction approaches, resulting in uncertainty in infrastructure development, service capacity, and resource allocation. This study aims to compare the performance of Random Forest and XGBoost algorithms in predicting the popularity of tourist destinations in Bali to support evidence-based decision-making. The research utilizes historical tourist visitation data from 2018 to 2023, obtained from the Bali Provincial Tourism Office. Data preprocessing includes data cleaning, normalization, feature encoding, and dimensionality reduction using Principal Component Analysis. Three data split schemes (80:20, 75:25, and 90:10) are evaluated. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that XGBoost outperforms Random Forest, achieving the best performance with a MAPE of 5.32% using the 90:10 data split. The selected model is then applied to project tourism demand for 2024–2026, indicating that nature-based and cultural tourism destinations remain dominant, particularly in Badung, Jembrana, and Gianyar Regencies. This study contributes to informatics by providing a machine-learning-based prediction model to support sustainable tourism management.]]>
<![CDATA[Hybrid LSTM Optimized by Genetic Algorithm for Accurate Gold Price Forecasting to Support Investment Decisions ]]>
<![CDATA[Chairunnisyah Widi Pratiwi]]>;
<![CDATA[Anjar Wanto]]>;
<![CDATA[Hendry Qurniawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5614
<![CDATA[Gold price volatility poses a major challenge for investors in making accurate and timely investment decisions, as price movements are influenced by complex and nonlinear financial dynamics. Conventional forecasting models often fail to capture these patterns optimally due to suboptimal parameter selection. This study aims to develop a hybrid forecasting model by optimizing the Long Short-Term Memory (LSTM) network using a Genetic Algorithm (GA) to improve gold price prediction accuracy. Historical gold price data spanning from 2004 to 2025, including Open, High, Low, and Close attributes, were utilized in this research. The methodological framework consists of data preprocessing, normalization, time-series sequence construction, dataset partitioning using an 80:20 training–testing ratio, and hyperparameter optimization through GA. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and prediction accuracy. Experimental results demonstrate that GA-based optimization significantly enhances LSTM performance, with RMSE reduced by more than 80%, accompanied by substantial decreases in MAE and MAPE, and an increase in prediction accuracy to 99.77%. These findings confirm that systematic hyperparameter optimization plays a crucial role in improving deep learning model generalization for financial time series forecasting. The proposed LSTM–GA model contributes to the development of more robust and reliable predictive frameworks for financial applications, particularly in supporting investment decision-making under volatile market conditions.]]>
<![CDATA[Parameter-Optimized Progressive Probabilistic Hough Transform Combined with Auto-CLAHE and Dual Gamma Correction for Night-Time Lane Detection in Autonomous Driving ]]>
<![CDATA[Nina Nur Aidha]]>;
<![CDATA[Esti Suryani]]>;
<![CDATA[Umi Salamah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.4.5628
<![CDATA[Traffic accidents are often caused by driver negligence or poor environmental conditions. Lane detection is a crucial component in autonomous vehicle technology and Advanced Driver Assistance Systems (ADAS) to improve driving safety by keeping the vehicle in its lane. However, lane detection at night often faces challenges due to low lighting and noise in the images. An accurate lane detection system is needed to address these issues so that safety features can function optimally. This research builds a night-time lane detection system by combining Automatic CLAHE with Dual Gamma Correction (ACLAHEwDGC) to improve image quality in low-light conditions and Progressive Probabilistic Hough Transform (PPHT) for lane line detection. The methodology includes preprocessing, segmentation, feature extraction, and a point-based distance evaluation. The system was evaluated using 284 frames from the Digital Image Media Lab Lane Detection Benchmark dataset and compared with previous methods using CLAHE and Standard Hough Transform (SHT). Through parameter optimization using Bayesian Optimization, the lane detection system achieved average values of 0.98 for Line Precision, 0.90 for Lane Recall, and 0.35 pixels for Distance Score, with an Overall Score of 0.96. These results show that optimizing traditional computer vision methods minimizes detection errors and provides a highly accurate results alternative to deep learning models for autonomous vehicle technology.]]>
