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
1,174 Documents
<![CDATA[Model-Driven Engineering for ARrupiah Cultural AR App: Kano Model and Qualitative User Experience Evaluation ]]>
<![CDATA[Gerson Feoh]]>;
<![CDATA[I Made Dwi Ardiada]]>;
<![CDATA[Gabriel Firsta Adnyana]]>;
<![CDATA[I Gede Hendrayana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5549
<![CDATA[The use of Augmented Reality (AR) in learning is becoming increasingly widespread, especially for introducing cultural and historical material in a more interesting way. However, many AR applications are still built without a structured design, making them difficult to develop when content is added. This study uses a Model-Driven Design (MDD) approach to organize the design of the ARrupiah application to make it more modular and easier to expand. After the prototype was completed, testing was conducted through surveys and interviews. The Kano survey involved 50 students to evaluate the main features of the application, while semi-structured interviews were analyzed using NVivo software to explore response patterns and user experiences, with a code saturation level of 80%. The survey results showed that around 70% of the features fell into the Attractive category, with a System Usability Scale (SUS) score of 82/100, indicating ease of use. Qualitative analysis reinforced the quantitative results through a triangulation process, in which features categorized as Attractive also emerged as a dominant theme of visual engagement in the NVivo results. This combined approach strengthens the validity of the findings and provides a more comprehensive understanding of user perceptions and satisfaction. Overall, the application of MDD not only helps refine the technical design but also improves the quality of the learning experience through ARrupiah-based interactive media.]]>
<![CDATA[Oil Palm Stem Disease Detection Based on Color Moments and GLCM Texture Features Using Artificial Neural Networks ]]>
<![CDATA[Hamdani Hamdani]]>;
<![CDATA[Anindita Septiarini]]>;
<![CDATA[Encik Akhmad Syaifudin]]>;
<![CDATA[Andi Tejawati]]>;
<![CDATA[Muhammad Zulfariansyah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5554
<![CDATA[Oil palm is an essential commodity for the economy; however, basal stem rot caused by Ganoderma boninense poses a significant threat to plantation productivity and long-term vitality. It highlights the importance of early detection of stem disease to facilitate timely intervention and minimize potential economic losses. This study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework. The dataset contained 525 images of oil palm stems, of which 205 depicted healthy specimens, and 320 depicted diseased ones. These were captured within their natural environment. Color features were derived by analyzing color moments within the HSV color space, while texture features were extracted from the Grey-Level Co-occurrence Matrix (GLCM). The extracted features were classified employing an Artificial Neural Network (ANN) and were subsequently contrasted with classifiers including Decision Tree, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine. Model performance was evaluated using k-fold cross-validation with k = 5 and k = 10 to ensure the consistency and reliability of the assessment. The experimental results demonstrated that the highest accuracy of 97.52% was achieved when the ANN model was used to classify the integrated color and texture features. The innovative aspect of this research resides in demonstrating that handcrafted features integrated with artificial neural networks can attain high detection accuracy in scenarios with limited data, providing a viable alternative to data-intensive deep learning techniques. This method facilitates a dependable, computer vision-driven early detection system for oil palm stem diseases, thereby promoting sustainable plantation management. ]]>
<![CDATA[Comparative Evaluation of ARIMA, LSTM, Hybrid ARIMA-GARCH, and Hybrid GARCH-LSTM Models for Daily Bitcoin and Gold Price Forecasting ]]>
<![CDATA[Isna Nurul Fatatik]]>;
<![CDATA[Asyifa Nur Fadhilah]]>;
<![CDATA[Irfan Adi Nugroho]]>;
<![CDATA[Muhammad Muflih Affandi]]>;
<![CDATA[Vriska Diah Novita Sari]]>;
<![CDATA[Shaifudin Zuhdi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5555
<![CDATA[The volatile nature of digital financial markets poses major challenges for predictive modelling, particularly in developing accurate forecasting models that can address diverse asset characteristics such as Bitcoin, with its extreme fluctuations, and Gold, which is known for its stable movements. This study addresses this challenge by evaluating the robustness of linear, deep learning, and hybrid architectures in both high-volatility and stable asset environments. Utilizing Bitcoin and Gold closing price data from 2022 to 2025, the methodology adopts a comparative workflow that involves ARIMA, ARIMA-GARCH, LSTM, and LSTM-GARCH Hybrid models. Stationarity (ADF) and heteroskedasticity (ARCH-LM) diagnostics alongside AIC/BIC selection criteria were applied, followed by a walk-forward validation scheme to assess the model's performance. Results confirmed that the hybrid GARCH-LSTM model delivered the lowest Root Mean Squared Error (RMSE), significantly outperforming single models by integrating statistical variance and temporal neural learning. Therefore, this study contributes to the field of computational intelligence by validating an accurate Artificial Intelligence (AI) framework for volatility-based forecasting and proposing a scalable blueprint for engineers to develop models that are capable of capturing the dynamics of financial time series data.]]>
<![CDATA[Bank Customer Churn Prediction Using CTGAN-Augmented Data and Boosting-Based Ensemble Learning with SHAP Explainable AI ]]>
<![CDATA[Mohamad Syazimmi Hersyaputra]]>;
<![CDATA[Shintami Chusnul Hidayati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5578
<![CDATA[Customer churn prediction remains a fundamental concern in the banking domain due to its direct impact on revenue stability and long-term customer value. A key challenge in churn modeling lies in severe class imbalance, which often limits model sensitivity toward minority churn cases. This study aims to develop an integrated and explainable churn prediction framework that effectively addresses class imbalance while maintaining robust predictive performance and interpretability. The proposed approach employs Conditional Tabular Generative Adversarial Networks (CTGAN), comparison of five boosting-based ensemble learning, and SHapley Additive exPlanations (SHAP) to preserve model interpretability. CTGAN is leveraged to synthesize high-fidelity instances for the churn class, yielding a class-balanced dataset that retains intricate tabular feature distributions. Five boosting-based ensemble models, XGBoost, CatBoost, Gradient Boosting Machine (GBM), Stochastic Gradient Boosting (SGB), and LightGBM, are systematically tuned using randomized hyperparameter optimization and evaluated under consistent experimental settings. Model performance is assessed using accuracy, precision, recall, and F1-score to capture classification performance under class imbalance. To ensure transparency, SHAP is applied to analyze global feature importance influencing churn predictions. Experimental results indicate CTGAN enhances model learning stability and detection capability. Among the evaluated models, CatBoost achieves the best results, with an accuracy of 0.9748 and an F1-score of 0.9178. The explainability analysis reveals that transactional features play a dominant role in churn. The novelty of this study lies in a unified and explainable churn prediction framework that integrates CTGAN-data augmentation, boosting ensembles, and interpretability for robust decision support in banking analytics.]]>
<![CDATA[Historical Image Restoration Using GFPGAN-Based Face-Centered Enhancement Mechanism to Address Blur and Low-Light Degradation ]]>
<![CDATA[Ardi Wijaya]]>;
<![CDATA[Rozali Toyib]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5580
<![CDATA[Archaic image restoration faces significant challenges due to complex degradation in the form of blurring and attenuation of extreme luminance (low-light) that obscure the identity of historical subjects. This study constructs a new paradigm through the Face-Centered Enhancement mechanism based on GFPGAN to reconstruct high-fidelity facial features in visual archives from the Bengkulu Museum, Bung Karno's Exile House, and Fort Marlborough. The novelty of this study lies in the integration of a feature enhancement module capable of performing adaptive deconvolution specifically on the face area to mitigate stochastic hallucinations in the GAN latent space, thus balancing lighting restoration without distorting the authenticity of the original character of historical figures. Quantitative evaluation of 50 images using a synthetic degradation scheme shows superior performance, where 95% of the data achieves SSIM ≥ 0.95 and MSE ≤ 0.01. This improvement in visual quality has direct implications for the efficiency of the OCR system in extracting document text and optimizing classification in digital archival information systems. Despite its dependence on high-performance computing, this method has proven effective in bridging the disparity between improving pixel quality and preserving historical integrity in national digital preservation efforts.]]>
<![CDATA[Implementing Proxmox VE-Based High Availability Clustering with Ceph Replication and Performance Testing for Resilient IT Infrastructure in High-Risk Disaster Areas ]]>
<![CDATA[Muhammad Abdul Muin]]>;
<![CDATA[Rahmawan Bagus Trianto]]>;
<![CDATA[Muhammad Nur Faiz]]>;
<![CDATA[Ratih Hafsarah Maharrani]]>;
<![CDATA[Satriawan Desmana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5591
<![CDATA[IT infrastructure in disaster-prone areas, particularly along Java's southern coastal region within the Sunda Arc subduction zone, faces significant vulnerability to seismic events and tsunamis that cause critical system downtime, disrupting emergency coordination and exacerbating disaster impacts. This study aims to develop and validate an open-source High Availability (HA) solution using Proxmox Virtual Environment (PVE) ensuring service continuity with Recovery Time Objective (RTO) under 2 minutes and near-zero Recovery Point Objective (RPO). The methodology encompasses four systematic stages: needs analysis identifying infrastructure requirements and disaster risk assessment for Cilacap region; architecture design implementing three-node PVE cluster with Ceph distributed storage (replication factor 3) and Corosync quorum mechanism; system implementation including network bonding, VLAN segmentation, and dedicated 1Gbps Ceph replication network; and comprehensive performance testing through fault injection scenarios (power-off simulation, network partition, storage failure) measuring inter-node latency, disk I/O performance, and failover recovery metrics. Results demonstrate exceptional reliability with 99.92% availability over 72-hour monitoring, Mean Time Between Failures (MTBF) of 24.1 hours, and Mean Time To Recovery (MTTR) of 70 seconds with total downtime of 3.53 minutes across three failover simulations. Inter-node latency remains below 1ms (average 0.372-0.593ms), while disk I/O latency maintains sub-0.5ms performance during failover events. This research contributes to computer science and disaster informatics by providing a validated, replicable open-source blueprint for resilient IT infrastructure in Indonesia's disaster-prone regions, offering practical implementation pathways for integration with national emergency systems including BNPB coordination networks and BMKG early warning infrastructure.]]>
<![CDATA[Global Inflation Forecasting Using Stacking Ensemble with Elastic Net Meta-Learner Integrating Random Forest, XGBoost, and LightGBM ]]>
<![CDATA[Fauriza Wildhani]]>;
<![CDATA[Anjar Wanto]]>;
<![CDATA[Irfan Sudahri Damanik]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5599
<![CDATA[Inflation dynamics have become increasingly complex due to economic volatility and nonlinear interactions, challenging the reliability of conventional forecasting models; therefore, this study develops a robust global inflation forecasting framework using a hybrid stacking ensemble that integrates Random Forest, XGBoost, and LightGBM as base learners with Elastic Net as a regularized meta-learner, applied to annual inflation data from 2000–2024 across five major economic blocs (G7, Europe, BRICS, ASEAN, and the Americas) after temporal feature engineering and time-series–preserving validation; the results demonstrate strong and consistent predictive performance, with very high accuracy in Europe (R² = 0.9282) and the G7 (R² = 0.9122), and the globally trained stacking model (R² = 0.7866) substantially outperforming the region-specific ASEAN model (R² = 0.5243), confirming the advantage of cross-country learning; this research advances informatics and computer science by providing a scalable and stable ensemble learning framework for macroeconomic time-series forecasting in volatile environments, supporting the development of AI-driven economic and policy analytics systems.]]>
<![CDATA[Forecasting Nutrient Concentration Dynamics in Hydroponic Lettuce Cultivation Using a Hybrid Fuzzy Time Series and Long Short-Term Memory Approach for Internet of Things–Based Systems ]]>
<![CDATA[Muh. Agus]]>;
<![CDATA[Alvian Tri Putra Darti Akhsa]]>;
<![CDATA[Ilham Ali Marka M]]>;
<![CDATA[Muhammad Fadel Hasyim]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5600
<![CDATA[Proper nutrient management is crucial for the optimal growth and yield of hydroponically cultivated lettuce. This study proposes a hybrid time-series forecasting model that integrates Fuzzy Time Series (FTS) and Long Short-Term Memory (LSTM) networks to predict nutrient concentration dynamics in hydroponic lettuce cultivation within an Internet of Things–based environment. Experimental data from four lettuce plant samples with different nutrient treatments (control, 400 PPM, 600 PPM, and 1000 PPM) were analyzed for 26 days, with the prediction extended to 40 days, representing the complete growth cycle using a TDS Sensor as a PPM value reader and a Solenoid Valve to accurately control the PPM value via ESP32 with Internet of Things (IoT) communication. This hybrid model incorporates growth-stage awareness through an adaptive weighting mechanism, resulting in a superior forecasting accuracy. The results showed that the ensemble approach achieved a Mean Absolute Percentage Error (MAPE) of 2.43% for the control, 3.12% for the 400 PPM, 3.45% for the 600 PPM, and 3.78% for the 1000 PPM sample. The 600 PPM treatment showed optimal development with 82% compliance with the recommended PPM range (560-840 ppm). The proposed model provides actionable insights for precision nutrient management, potentially reducing fertilizer use by 23-35% while maintaining crop quality. This study contributes to hybrid intelligent systems and time-series forecasting by demonstrating an effective integration of rule-based fuzzy modeling and deep recurrent neural networks in Internet of Things–driven environments for hydroponic systems, supporting efficient resource utilization and increased crop productivity.]]>
<![CDATA[Comparative Evaluation of Linear Regression and Ensemble Learning Models for Daily Calorie Prediction Using a Public Lifestyle Dataset with Structured Preprocessing and Recursive Feature Elimination ]]>
<![CDATA[Yunandra Wahyu Utama]]>;
<![CDATA[Majid Rahardi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5621
<![CDATA[Accurate daily calorie estimates are essential for personalized nutrition and prevention of diet-related conditions, yet lifestyle variability can reduce the effectiveness of one-size-fits-all recommendations. This study aims to develop an accurate lifestyle-based calorie estimation model by comparing an interpretable linear approach with ensemble machine learning methods. A publicly available lifestyle dataset from Kaggle was used, containing demographic variables, anthropometric measurements, food intake, dietary patterns, and physical activity attributes. A preprocessing pipeline was applied, including outlier handling using interquartile range capping, categorical encoding, normalization, and feature selection via Recursive Feature Elimination to identify the most relevant predictors. Four models (Linear Regression, Random Forest, XGBoost, and LightGBM) were trained and evaluated, followed by hyperparameter tuning of ensemble models using GridSearchCV. Performance was assessed using R², Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) and training time. Linear Regression achieved the best overall performance (R² = 0.9650, MAE = 80.95, RMSE = 101.71, training time = 8.95 seconds). Among ensembles, the tuned XGBoost performed best (R² = 0.9646, MAE = 81.34, RMSE = 102.35, training time = 10.55 seconds). Compared with tuned XGBoost, Linear Regression was superior with MAE by 0.39 and RMSE by 0.64, while R² increased by 0.0004 and required less computational time, indicating that added complexity did not yield meaningful gains on this structured dataset. These findings suggest that, for structured lifestyle data, interpretable linear models can match or outperform complex ensembles while remaining computationally efficient for real-time or edge-deployed health applications.]]>
<![CDATA[Performance Comparison Of K-Nearest Neighbors And Decision Tree Algorithms With Random Oversampling For Imbalanced Heart Disease Classification ]]>
<![CDATA[Dita Yustianisa]]>;
<![CDATA[Farid Wajidi]]>;
<![CDATA[Wawan Firgiawan]]>;
<![CDATA[Adinda Gama Sholeha]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5626
<![CDATA[Heart disease remains one of the leading causes of mortality worldwide, including in Indonesia, where delayed detection continues to be a serious challenge. In medical data mining, class imbalance often degrades classification performance by reducing sensitivity toward minority class cases. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Decision Tree algorithms for heart disease classification and to evaluate the effectiveness of random oversampling in handling imbalanced data. This research uses a heart disease dataset consisting of 10,000 medical records obtained from Kaggle. Data preprocessing includes categorical transformation, missing value imputation using KNN Imputer, and Min–Max normalization. Random oversampling is applied to increase minority class representation. Model evaluation is performed using stratified 10-fold cross-validation with accuracy, precision, recall, F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) as performance metrics. Experimental results show that after random oversampling, the KNN model achieves the best performance with an accuracy of 94%, precision of 96%, recall of 90%, F1-score of 92%, and ROC–AUC of 90.2%. In comparison, the Decision Tree model records an accuracy of 80%, precision of 78%, recall of 81%, F1-score of 79%, and ROC–AUC of 81.5%. These findings demonstrate that random oversampling significantly improves minority class detection, particularly for KNN. This study contributes to Informatics by providing empirical evidence that simple and efficient data mining strategies can effectively address class imbalance in large-scale medical datasets, supporting the development of accurate, interpretable, and accessible AI-based diagnostic systems for early heart disease detection.]]>
