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
962 Documents
<![CDATA[Development of a Smart Environment Maturity Model for Green Industry in North Maluku's Mining Villages, Indonesia ]]>
<![CDATA[Arief, Assaf]]>;
<![CDATA[Apriyanto, Heri]]>;
<![CDATA[Muhammad, Miftah]]>;
<![CDATA[Harisun, Endah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5107
<![CDATA[The smart environment maturity model for sustainable mining village areas in North Maluku Province has become a primary demand for the transformation towards sustainable green smart villages. North Maluku, one of Indonesia's largest mining industry provinces, includes the Halmahera and Obi archipelagos as sources of nickel, iron ore/sand, gold, and silver mines. This study aims to develop a maturity model that integrates Indonsian regulations to support green industry implementation in mining villages. The methodology employs Systematic Literature Review to identify Critical Success Factors (CSFs), validated through expert judgment using 5-point Likert scale assessment. The research results yield eight key dimensions, 25 sub-dimensions, and five maturity levels: underdeveloped, developing, self-reliant, advanced, and smart villages. Expert validation achieved an overall average score of 3.65/5.0, indicating moderate acceptance with improvement areas identified in local culture and technology dimensions. The developed framework provides a foundation for environmental informatics applications and decision support systems in rural development contexts. The model addresses national regulations concerning green industry while providing an adaptive framework for archipelago regions, serving as a reference for policy formulation and village fund allocation based on environmental indicators.]]>
<![CDATA[Enhancing Accessibility in Local Government Data Portals via Retrieval- Augmented Generation: A Case Study on Satu Data Indonesia in Banyumas Regency ]]>
<![CDATA[Hadie, Agus Nur]]>;
<![CDATA[Tahyudin, Imam]]>;
<![CDATA[Hariguna, Taqwa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5153
<![CDATA[Public access to local government data in Indonesia, such as that in the Satu Data Indonesia portal for Banyumas Regency, is severely hampered by outdated search interfaces and the technical complexity of handling heterogeneous data formats like PDF, Excel, and CSV. This research directly addresses this accessibility gap by designing, developing, and evaluating an intelligent question-answering system. We introduce a novel application of a Retrieval- Augmented Generation (RAG) architecture tailored for Indonesian local government data. The core novelty lies in our methodology for handling heterogeneous data formats (PDF, Excel, CSV) by integrating a low-code orchestrator (n8n) with a high-performance vector database (pgvector), a practical solution for a common public sector challenge. The system utilizes the text-embedding-3-large model for semantic understanding and gpt-4.1 for generating grounded, factual answers. The system's effectiveness was rigorously validated, achieving a perfect 100% score across accuracy, precision, recall, and F1-score on defined test cases. Crucially, usability testing with end-users confirmed the system is perceived as significantly more efficient and user-friendly than manual data searching. The primary impact of this work is a validated, replicable blueprint for local governments to democratize public information. By transforming complex data retrieval into an intuitive conversation, this research offers a practical AI solution to enhance governmental transparency and citizen engagement.]]>
<![CDATA[Optimizing Alternating Least Squares for Recommender Systems Using Particle Swarm Optimization ]]>
<![CDATA[Yusfida A'la, Fiddin]]>;
<![CDATA[Firdaus, Nurul]]>;
<![CDATA[Supriyadi, Andy]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5154
<![CDATA[Recommender systems play a crucial role in various digital platforms by assisting users in discovering relevant items. The research problem addressed in this study is the limited predictive accuracy of ALS-based recommender systems due to suboptimal parameter selection. This study explores how Particle Swarm Optimization (PSO) can be leveraged for parameter optimization to address this limitation. The dataset used is MovieLens 1M, which contains over one million user ratings for thousands of movies. The research process includes data preprocessing, data splitting, model training, and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) as the primary metrics. The evaluation results indicate a significant improvement in model performance after optimization, with RMSE decreasing from 0.895 to 0.860 and MAE from 0.704 to 0.680. These findings demonstrate that optimization algorithms can effectively improve the prediction accuracy of recommendation systems. This research contributes to the application of swarm-based optimization techniques in enhancing matrix factorization-based recommender systems.]]>
<![CDATA[Optimizing Type 2 Diabetes Classification with Feature Selection and Class Balancing in Machine Learning ]]>
<![CDATA[Wantoro, Agus]]>;
<![CDATA[Yuliana, Aviv Fitria]]>;
<![CDATA[Andini, Dwi Yana Ayu]]>;
<![CDATA[Awaliyani, Ikna]]>;
<![CDATA[Caesarendra, Wahyu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5166
<![CDATA[Type 2 Diabetes (T2DM) is a crucial factor in patient survival and treatment effectiveness. Errors in diabetes detection lead to disease severity, high costs, prolonged healing time, and a decline in service quality. Additionally, a major challenge in developing Machine Learning (ML)-based detection decision support systems is the class imbalance in medical data as well as the high feature dimensionality that can affect the accuracy and efficiency of the model. This research proposes an approach based on feature selection (FS) and handling class imbalance to improve performance in type 2 diabetes. Several feature selection techniques such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), Chi-Square (CS), Relief-F, and FCBF can perform feature selection based on weighting ranking. Furthermore, to address the imbalanced class distribution, we utilize the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as Support Vector Machine (SVM), Gradient Boosting (GB), Tree, Neural Network (NN), Random Forest (RF), and AdaBoost were tested and evaluated based on the confusion matrix including accuracy, precision, recall, and time. The experimental results show that the combination of strategies for handling imbalanced classes significantly improves the predictive performance of ML algorithms. In addition, we found that the combination of feature selection techniques IG+AdaBoost consistently demonstrates optimal performance. This study emphasizes the importance of data preprocessing and the selection of the right algorithms in the development of machine learning-based T2DM detection systems. Accurate detection can reduce the severity of disease, lower treatment costs, speed up the healing process, and improve healthcare services.]]>
<![CDATA[An Enhanced Particle Swarm Optimization with Mutation for Mean-Value-at-Risk Portfolio Optimization in the Indonesian Banking Sector ]]>
<![CDATA[Anam, Syaiful]]>;
<![CDATA[Bukhori, Hilmi Aziz]]>;
<![CDATA[Maulana, Avin]]>;
<![CDATA[Maulana, M. Idam]]>;
<![CDATA[Rasikhun, Hady]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5191
<![CDATA[Portfolio optimization in emerging markets is challenging because high volatility and non-normal return distributions reduce the effectiveness of traditional mean–variance models, which tend to underestimate downside risk. This study aims to develop and evaluate an Enhanced Particle Swarm Optimization with Mutation (PSO with Mutation) for portfolio optimization under the Mean-Value-at-Risk (Mean-VaR) framework in the Indonesian banking sector. The novelty of this approach lies in integrating a mutation operator into standard PSO to maintain population diversity, prevent premature convergence, and improve exploration of the solution space. To evaluate the method, daily adjusted closing prices of 31 Indonesian bank stocks from January 2020 to July 2025 were collected. Preprocessing included removing tickers with incomplete data and computing daily returns. The optimization problem was formulated using Mean-VaR as the risk measure, with portfolio weight constraints. The proposed PSO with Mutation was benchmarked against standard PSO, Genetic Algorithm (GA), Bat Algorithm (BA), BA with Mutation, and classical models (Markowitz and Monte Carlo–based VaR). Performance was assessed using expected return, Mean-VaR, risk-adjusted return, Sharpe ratio, execution time, and stability across 25 independent runs. The results show that PSO with Mutation achieved a competitive expected return (0.0020), the lowest Mean-VaR (0.0311), the highest risk-adjusted return (0.0650), and the lowest variability across runs, while maintaining acceptable execution time. These findings confirm that mutation-enhanced PSO provides a robust, balanced, and efficient solution for portfolio optimization, making it highly relevant for investors in volatile emerging markets and advancing research on hybrid metaheuristics in financial optimization.]]>
<![CDATA[Design and Implementation of a Solar-Powered Pest Repellent System for Shallot Farms Using Ultrasonic, and Light Emitters Based on ESP32 ]]>
<![CDATA[Hutajulu, Albert Gifson]]>;
<![CDATA[Daffa, Dhimas Alviero]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5198
<![CDATA[Shallots are an agricultural commodity of high economic value that often experiences pest attacks, causing a decrease in crop yields. Commonly used pest control methods, such as pesticides and manual expulsions, are often less effective and can have a negative impact on the environment. Therefore, this research aims to design and develop a pest repellent based on the ESP32 microcontroller that can be operated in automatic or manual mode and utilize the resources of the Solar Power Plant (PLTS) supported by ultrasonic loads and LED lights. The research methods used include device design using PCB, load testing, and system performance analysis based on the voltage and current generated. The test results show that the system has high stability during the day with an average voltage of 12.54V and an average current of 0.07A for ultrasonic loads. However, at night, the system degrades, because the solar cells do not produce energy. The stability of the system is also more optimal at the use of ultrasonic loads compared to LED loads which exhibit higher voltage fluctuations. Thus, this tool can be an effective and environmentally friendly solution for shallot farmers in reducing pest attacks. By providing a performance analysis of different actuators under solar-powered constraints, this research contributes to the development of low-power, autonomous IoT system for smart agriculture.]]>
<![CDATA[Stacking-Based Support Vector Machine and Multilayer Perceptron for Dysarthria Detection Using MFCC Features ]]>
<![CDATA[Pujiyanta, Ardi]]>;
<![CDATA[Noviyanto, Fiftin]]>;
<![CDATA[Ismail, Taufiq]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5199
<![CDATA[The manual diagnosis of dysarthria is often time-consuming and requires the expertise of trained specialists, which can delay early intervention and treatment. This study aims to develop an automated detection system to improve diagnostic accuracy and efficiency. Mel-Frequency Cepstral Coefficients (MFCC) are used as the primary features, and three classification models are evaluated: Support Vector Machine (SVM), Multilayer Perceptron (MLP), and a stacking ensemble that combines both. The evaluation is conducted on a dataset of 240 audio samples. Experimental results show that the stacking ensemble achieves the highest performance, with an accuracy of 97.92%, surpassing SVM (95.83%) and MLP (93.75%). These findings highlight the significant potential of voice-based classification to accelerate dysarthria diagnosis, thus supporting clinical screening and speech therapy applications.]]>
<![CDATA[A Literature-Based Heat Matrix for Quantifying Inter-Domain Correlations within the ISO/IEC 27002:2013 Framework ]]>
<![CDATA[Dazki, Erick]]>;
<![CDATA[Indrajit, Richardus Eko]]>;
<![CDATA[Dio F, Januponsa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5203
<![CDATA[The problem of managing information security controls is complex because the domains outlined in standards like ISO/IEC 27002 rarely operate in isolation; they have intricate interdependencies that are often overlooked. This oversight can lead to fragmented security controls, inefficient resource allocation, and weaknesses in overall security governance. To address this issue, this paper proposes a literature-based heat matrix methodology, building on ISO/IEC 27002:2013 while referencing the updated 2022 guidance, NIST SP 800-53 Revision 5, and COBIT 2019. The primary goal is to assign numerical correlation values to the fourteen domains of ISO/IEC 27002:2013, providing a structured approach to visualize and understand their interrelationships. The methodology involves a comprehensive literature review and is complemented by expert validation from experienced practitioners to refine the correlation scores. The result is an illustrative 14x14 matrix that demonstrates how numeric inter-domain correlations can reveal critical overlaps and guide strategic decision-making. A new five-tier correlation scale is introduced to aid interpretation, clarifying whether two domains have very low, low, moderate, high, or very high levels of interdependency. This approach offers a significant impact on the field of informatics and computer science by enabling organizations to move beyond siloed security management. By recognizing these correlations, organizations can allocate resources more effectively, enhance holistic risk management, and strengthen security governance. The heat matrix serves as a practical tool for practitioners and managers to identify domain pairs that require close coordination, ultimately leading to more coherent policy frameworks and a more robust security posture.]]>
<![CDATA[Geographically Weighted Random Forests for Human Development Index of Central Java Prediction ]]>
<![CDATA[Zuhdi, Shaifudin]]>;
<![CDATA[Fatatik, Isna Nurul]]>;
<![CDATA[Prihasno, Izlah Nur Fadlila Herawati]]>;
<![CDATA[Rozaq, Hasri Akbar Awal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5204
<![CDATA[The geographically weighted regression (GWR) model has been widely used in various types of predictions, including human development index predictions. Similarly, the random forests (RF) model has also been widely used in various value predictions. The GWR model always assumes a local linear relationship between dependent and independent variables. The RF model only produces one global model that cannot represent conditions at each location. The GWR model is susceptible to multicollinearity in each independent variable, which can lead to overfitting if multicollinearity in the model is high. To address the vulnerability of the GWR model to multicollinearity, the RF model and the GWR model can be combined. Since the RF model is not vulnerable to multicollinearity in the independent variables, the modification becomes the geographically weighted random forests (GWRF) model to improve the shortcomings of the GWR and RF models. The GWR and GWRF models were constructed using data from districts and cities in Central Java Province, which was selected as the study area due to evident disparities in human development index achievements. These disparities highlight the presence of spatial heterogeneity that conventional models fail to adequately capture. To rigorously evaluate model performance, data from 2023 were employed as training data, while data from 2024 served as testing data. This research introduces a novel integration of spatial econometric and machine learning approaches, providing a more robust framework for addressing complex spatial variations in human development outcomes. The GWRF model is capable of producing a model that does not overfit when there is multicollinearity among independent variables. The GWRF model offers a novel integration of machine learning and spatial modelling, outperforming both GWR and RF by not only delivering high predictive accuracy under complex variable relationships but also capturing nuanced local spatial heterogeneity that conventional approaches fail to address.]]>
<![CDATA[GWO-Enhanced Hybrid Deep Learning with SHAP for Explainable TLKM.JK Stock Forecasting ]]>
<![CDATA[Bukhori, Hilmi Aziz]]>;
<![CDATA[Bukhori, Saiful]]>;
<![CDATA[Anam, Syaiful]]>;
<![CDATA[Yusuf, Feby Indriana]]>;
<![CDATA[Sari, Meylita]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5205
<![CDATA[This study presents an innovative Grey Wolf Optimization (GWO)-enhanced hybrid deep learning model integrating Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer, combined with SHAP for interpretable stock price forecasting of TLKM.JK from July 29, 2024, to July 29, 2025. Addressing non-linear market dynamics, the model evaluates seven experimental cases, with the GWO-optimized configuration (Case 2) achieving superior performance, with a Root Mean Squared Error (RMSE) of 75.23, Mean Absolute Error (MAE) of 58.14, and Directional Accuracy (DA) of 76.2%, surpassing the baseline by 17.4% in RMSE and 8.1% in DA. Notably, Case 2 excels during the April 2025 surge (11.8% increase, MAE 53, DA 82%) and the high-volume day of May 28, 2025 (531,309,500 shares, MAE 48), leveraging Volume (SHAP 0.45) and RSI (0.28) as key predictors. With a 4-hour convergence time on an NVIDIA RTX 3060 GPU, the model ensures computational efficiency and interpretability, making it a robust tool for traders. Despite limitations in single-stock focus and GPU dependency, this framework advances AI-driven financial forecasting by offering transparent, high-accuracy predictions, paving the way for multi-stock applications and real-time SHAP updates.]]>
