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
1,048 Documents
<![CDATA[Hybrid LSTM Forecasting Framework with Mutual Information and PSO–GWO Optimization for Short-Term SARS-CoV-2 Prediction in Indonesia ]]>
<![CDATA[Nastiti, Faulinda Ely]]>;
<![CDATA[Musa, Shahrulniza]]>;
<![CDATA[Riadi, Imam]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5485
<![CDATA[SARS-CoV-2 remains an endemic challenge in Indonesia, requiring reliable short-term forecasting tools that support informatics, digital epidemiology, and data-driven public health systems. Standard LSTM models, while widely used for epidemic forecasting, face notable limitations such as sensitivity to poor weight initialization, and reduced ability to capture interactions within heterogeneous high-dimensional data—resulting in inconsistent performance. This research introduces ADELMI (Adaptive Deep Learning Metaheuristic Intelligence), a unified hybrid forecasting framework specifically designed not only to enhance forecasting accuracy but also to overcome core weaknesses of traditional LSTM architectures when applied to complex epidemic datasets. ADELMI integrates Mutual Information and Pearson Correlation for dual feature selection with a hybrid Particle Swarm–Grey Wolf Optimization (PSO–GWO) approach for optimizing LSTM parameters. The dataset includes 657 daily observations and 82 epidemiological, vaccination, and meteorological variables sourced from the Ministry of Health and BMKG (2020–2021). Feature selection reduced the dataset to 20 relevant predictors for recovery and death and one dominant predictor for positive cases. The optimized 50-unit LSTM with early stopping achieved highly accurate 7-day forecasts, producing MAPE scores of 0.01% (positive cases), 1.44% (recoveries), and 3.00% (deaths) across 5-fold cross-validation. These results significantly outperform ARIMA, SIR, and baseline LSTM models. By unifying dual feature selection with hybrid PSO–GWO optimization, ADELMI improves LSTM stability, weight initialization, and multivariate interaction modeling, delivering more reliable forecasts across heterogeneous datasets. This advancement strengthens informatics through DL-metaheuristic multivariate epidemic modeling and enables proactive, adaptive surveillance against evolving threats such as influenza hybrids.]]>
<![CDATA[Preventing Data Leakage in Classification via Integrated Machine Learning Pipelines: Preprocessing, Feature Transformation, and Hyperparameter Tuning ]]>
<![CDATA[Ichwani, Arief]]>;
<![CDATA[Kesuma, Rahman Indra]]>;
<![CDATA[Setiawan, Andika]]>;
<![CDATA[Wicaksono, Imam Eko]]>;
<![CDATA[Hanifah, Raidah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5490
<![CDATA[Data leakage in machine learning classification often leads to overfitting, inflated performance estimates, and poor reproducibility, which can undermine the reliability of deployed models and incur industrial losses. This paper addresses the leakage problem by proposing an integrated machine learning pipeline that strictly isolates training and evaluation processes across preprocessing, feature transformation, and model optimization stages. Experiments are conducted on the Titanic passenger survival dataset, where exploratory data analysis identifies data quality issues, followed by stratified train-test splitting to preserve class distribution. All preprocessing steps, including missing value imputation, categorical encoding, and feature scaling, are applied exclusively to the training data using a ColumnTransformer embedded within a unified Pipeline. A K-Nearest Neighbors (KNN) classifier is employed, with hyperparameters optimized via GridSearchCV and 3-fold cross-validation. Experimental results show that a baseline model without leakage control achieves only 72.62% test accuracy and exhibits a substantial overfitting gap. In contrast, the proposed pipeline-based approach improves generalization, achieving 78.21% test accuracy with an optimal configuration of k = 29 and Manhattan distance while significantly reducing overfitting. The main contribution of this work is the formulation of a reproducible, leakage-aware pipeline guideline that ensures unbiased evaluation and reliable generalization in classification tasks, providing practical methodological insights for both academic research and real-world machine learning applications.]]>
<![CDATA[Benchmarking Relational and Array-Based Models for Genealogical Data Storage in PostgreSQL ]]>
<![CDATA[Raharjo, Suwanto]]>;
<![CDATA[Utami, Ema]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5629
<![CDATA[Genealogical information systems manage inherently hierarchical data structures that represent family relationships across multiple generations. Traditional implementations predominantly rely on normalized relational database designs using junction tables to model parent–child relationships. While this approach ensures strong referential integrity, it often incurs substantial performance overhead due to complex join operations during deep hierarchical traversal. Recent versions of PostgreSQL provide native support for array data types This study compares two genealogical database models implemented in PostgreSQL: a normalized relational model using a junction table and a denormalized model that stores child identifiers directly as UUID arrays. To evaluate their performance, we conducted controlled benchmarking experiments using synthetically generated genealogical datasets with varying generational depth and branching patterns. The comparison focuses on storage efficiency, recursive traversal performance, and write operation costs under realistic hierarchical workloads. Results obtained from a large-scale dataset containing more than 7 million individual records show that the UUID array–based model reduces disk space usage by 31%. During deep recursive traversal involving over 12 million nodes at the tenth generation, the array-based model demonstrates improved data locality, leading to a 5.2% reduction in execution latency and 7% fewer shared buffer accesses compared to the relational model. Interestingly, contrary to common expectations in normalized database design, the array-based model achieves 22% faster single-insert performance because it avoids foreign key validation and multiple index updates. This improvement comes with slightly higher write amplification, reflected in a 6.6% increase in buffer usage due to PostgreSQL’s multi-version concurrency control mechanism. These findings contribute to the field of Informatics by providing empirical evidence on how database internal mechanisms influence performance trade-offs in hierarchical data management, offering guidance for designing scalable and read-efficient information systems beyond genealogical applications.]]>
<![CDATA[A Hybrid Decision Support Framework for Food and Nutrition Security Assessment Using Multi-Criteria Decision Making and Machine Learning ]]>
<![CDATA[Solikin, Solikin]]>;
<![CDATA[Wicaksono, Harjunadi]]>;
<![CDATA[Setyarini, Tri Ana]]>;
<![CDATA[Khumaidi, Ali]]>;
<![CDATA[Darmawan, Risanto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5474
<![CDATA[Food and nutrition security assessment requires an adaptive analytical approach due to the multidimensional and temporal complexity of food systems. This study proposes a hybrid decision support system integrating Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with machine learning to evaluate and predict food security indicators dynamically. Panel data from West Java and East Nusa Tenggara for the period 2018–2024 were analyzed to capture structural and temporal characteristics. AHP was used to determine expert-based indicator weights, which were applied in TOPSIS to generate regional food security scores. These scores were subsequently modeled using machine learning with temporal feature engineering, including lag variables and rolling statistics, and evaluated using time-series cross-validation. The results reveal a strong negative correlation (−0.7398) between AHP weights and machine learning feature importance, indicating complementary expert-based and data-driven perspectives. Ridge Regression achieved the best predictive performance with an R² of 0.9983 on training data and 0.8186 under cross-validation. East Nusa Tenggara outperformed West Java in TOPSIS scores (0.4829 vs. 0.4626), highlighting the importance of food stability and utilization. This study advances Informatics by enabling dynamic and adaptive food security decision support.]]>
<![CDATA[Spatial Information System for Housing Data Collection at The Department Of Public Housing and Settlement Areas ]]>
<![CDATA[Vatresia, Arie]]>;
<![CDATA[Utama, Ferzha Putra]]>;
<![CDATA[Sugianto, Nanang]]>;
<![CDATA[Turrahma, Ridha Nafila]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5513
<![CDATA[The Bengkulu City Public Housing and Settlement Agency is a government agency tasked with assisting the mayor in administering government affairs related to public housing, settlement areas, and land matters under regional jurisdiction. In addition, the Department of Public Housing and Settlement Areas also has an obligation to serve the community and always strives to provide the best service information to the community, especially in Bengkulu City. This is particularly true in terms of housing data collection processed by the Department of Public Housing and Settlement Areas in Bengkulu City. The housing data collection system used for office purposes in terms of monthly or annual reports and archives at the Public Housing and Settlement Agency is not yet computerized. However, to date, these main activities are still carried out manually. The storage of this data is also still piled up in cabinets, which requires a long time to create reports and search for the data itself. This need can be facilitated by the existence of computational products and algorithms in the Informatics program that synergize with Geophysics in addressing data collection and landscape analysis issues related to housing and settlements. The implementation of this integrated system improves the management process of plot pattern data, basic infrastructure, uninhabitable house, and developer. This research showed that the total calculation, the average System Usability Testing score obtained was 62.5, which falls under the good category. This indicates that the developed system meets usability standards and is well accepted by its users.]]>
<![CDATA[Regression Based Prediction of Roblox Game Popularity Using Extreme Gradient Boosting with Hyperparameter Optimization ]]>
<![CDATA[Amalina, Inna Nur]]>;
<![CDATA[Norhikmah, Norhikmah]]>;
<![CDATA[Ariyus, Dony]]>;
<![CDATA[Koprawi, Muhammad]]>;
<![CDATA[Prasetyo, Rafli Ilham]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5648
<![CDATA[The rapid growth of the digital gaming industry has increased the importance of predicting game popularity on user-generated content platforms such as Roblox, where diverse games and highly variable user engagement patterns create challenges in modeling long-term popularity trends. This study aims to develop a regression-based popularity prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on user interaction indicators, including visits, likes, dislikes, favorites, and active players. To investigate the effect of model optimization, hyperparameter tuning is performed using GridSearchCV. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Experimental results show that the baseline XGBoost model achieves an R² value of 80.74%, indicating strong capability in capturing non-linear popularity patterns. However, the optimized model yields a lower R² value of 77.71%, accompanied by slight increases in prediction error metrics, revealing that hyperparameter optimization does not always improve performance for highly skewed popularity data. Feature importance analysis further indicates that interaction-based attributes, particularly likes and dislikes, are the most influential predictors. These findings provide an important contribution to Informatics research by demonstrating the effectiveness of ensemble regression models for digital entertainment analytics while highlighting the need for critical evaluation of optimization strategies rather than assuming universal performance gains.]]>
<![CDATA[Cardiovascular Disease Risk Prediction Using Random Forest, RFECV Feature Selection, and SHAP with Multisource Clinical Data Integration ]]>
<![CDATA[Fania, Dea]]>;
<![CDATA[Waspada, Indra]]>;
<![CDATA[Wibawa, Helmie Arif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5744
<![CDATA[Cardiovascular disease (CVD) remains one of the leading causes of mortality in Indonesia, highlighting the urgent need for effective preventive strategies, including the development of risk prediction systems based on population health data. A major challenge in developing CVD prediction models is the limited availability of local medical data that adequately represent the Indonesian population. This study aims to develop a CVD risk prediction model using the Random Forest algorithm by integrating two data sources: private clinical data from cardiology outpatients at RSUD M. Yunus Bengkulu and a publicly available dataset. Data integration was conducted to address the limited size of private data and to improve model performance. The research was conducted through three experimental settings. Shapley Additive Explanations (SHAP) were employed to analyze the contribution of each feature, while Recursive Feature Elimination with Cross-Validation (RFECV) was applied for feature selection. The results indicate that Scenario 3 in the Experiment on Data Integration achieved the best performance, with an accuracy of 73.57%, recall of 81.44%, and F1-score of 77.06%. SHAP analysis identified blood pressure and age as the most influential predictors of CVD risk. These findings demonstrate that integrating limited private data with public datasets can significantly improve model performance while providing clinically interpretable insights, particularly in settings with constrained local data availability.]]>
<![CDATA[Performance Comparison Of Xgboost Lightgbm And Lstm For E-Commerce Repeat Buyer Prediction ]]>
<![CDATA[Nugroho, Lustiyono Prasetyo]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Utomo, Fandy Setyo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.5746
<![CDATA[Repeat buyer behavior is a critical indicator of customer retention success in e-commerce platforms. However, accurately predicting repeat buyers remains a challenging problem due to the complexity of user behavior patterns and the temporal characteristics embedded in interaction data. Existing studies often focus on single modeling approaches or limited sequence exploration, resulting in insufficient comparative insight between ensemble-based machine learning and sequence-based deep learning models. Therefore, this study aims to systematically compare the performance of tree-based ensemble models (XGBoost and LightGBM) and a sequence-based deep learning model (LSTM) in predicting repeat buyers using user behavior data. To ensure fair evaluation, data preprocessing and feature engineering were carefully designed to prevent data leakage by utilizing user behavior prior to the first purchase. Model performance was evaluated using Accuracy, F1-score, and ROC–AUC metrics. Experimental results show that XGBoost and LightGBM achieve stable classification performance with accuracy values of 86.11% and 85.84%, respectively, while the LSTM model attains the highest ROC–AUC value of 0.937, indicating superior capability in capturing temporal behavioral patterns. This study provides valuable insights for e-commerce platforms seeking to optimize predictive models for repeat buyers, contributing to more effective customer retention strategies.]]>
