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.
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962 Documents
<![CDATA[Deep Reinforcement Learning for Autonomous System Optimization in Indonesia: A Systematic Literature Review ]]>
<![CDATA[Yusuf, Dedi]]>;
<![CDATA[Supraptono, Eko]]>;
<![CDATA[Suryanto, Agus]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4446
<![CDATA[Background: The development of artificial intelligence (AI) technology, including Deep Reinforcement Learning (DRL), has brought significant changes in various industrial sectors, especially in autonomous systems. DRL combines the capabilities of Deep Learning (DL) in processing complex data with those of Reinforcement Learning (RL) in making adaptive decisions through interaction with the environment. However, the application of DRL in autonomous systems still faces several challenges, such as training stability, model generalization, and high data and computing resource requirements. Methods: This study uses the Systematic Literature Review (SLR) method to identify, evaluate, and analyze the latest developments in DRL for autonomous system optimization. The SLR was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, which consists of four main stages: identification, screening, eligibility, and inclusion of research articles. Data were collected through literature searches in leading scientific journal databases such as IEEE Xplore, MDPI, ACM Digital Library, ScienceDirect (Elsevier), SpringerLink, arXiv, Scopus, and Web of Science. Results: This study found that DRL has been widely adopted in various industrial sectors, including transportation, industrial robotics, and traffic management. The integration of DRL with other technologies such as Computer Vision, IoT, and Edge Computing further enhances its capability to handle uncertain and dynamic environments. Therefore, this study is crucial in providing a comprehensive understanding of the potential, challenges, and future directions of DRL development in autonomous systems, in order to foster more adaptive, efficient, and reliable technological innovations.]]>
<![CDATA[Real-Time Rice Leaf Disease Diagnosis: A Mobile CNN Application with Firebase Integration ]]>
<![CDATA[Azis, Abdul]]>;
<![CDATA[Fadlil, Abdul]]>;
<![CDATA[Sutikno, Tole]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4452
<![CDATA[Rice, the staple food for the majority of Indonesia's population, faces significant production threats from leaf diseases, which can decrease yields and jeopardize national food security. Traditional manual identification of these diseases is a major challenge for farmers, as it is often subjective, prone to misdiagnosis leading to incorrect treatments, time-consuming, demands specialized expertise, and is difficult to implement widely for effective real-time early prevention, allowing diseases to spread and significantly impact crop yields. This research addresses these challenges by developing an automated and easily accessible rice leaf disease diagnosis system. The system is manifested as a mobile application that integrates a Convolutional Neural Network (CNN) model, specifically utilizing the EfficientNetB0 architecture, for the classification of rice leaf images and leverages key Firebase services such as its Realtime Database for data synchronization and Cloud Storage for image management to ensure a scalable and responsive backend. The methodology involved several key stages. Firstly, the CNN model was developed by employing a transfer learning approach on the pre-trained EfficientNetB0 architecture. Secondly, the model underwent comprehensive testing using a dataset of 1,000 new rice leaf images, which were independently validated by agricultural experts. The results demonstrated that the developed CNN model achieved a global accuracy of 85.9%, with an average precision of 86.1% and recall of 85.9% (macro-average) in the expert validation testing phase with the 1,000 new images. However, the study also identified variations in the model's performance across different disease classes, highlighting areas that require further optimization to enhance detection effectiveness for specific types of rice leaf diseases. The primary benefit of this research is the provision of a practical rice leaf disease diagnosis tool that is readily accessible to farmers via a mobile application, empowering them with timely and accurate information for effective crop management. This can lead to reduced crop losses, improved yield quality, and contribute significantly to national food security. Furthermore, this research contributes to the field of applied machine learning and mobile computing in resource-constrained agricultural environments, offering valuable insights for the development of impactful informatics solutions.]]>
<![CDATA[Enhancing Monkeypox Skin Lesion Classification With Resnet50v2: The Impact Of Pre-Trained Models From Medical And General Domains ]]>
<![CDATA[Azhar, Saifulloh]]>;
<![CDATA[Syukur, Abdul]]>;
<![CDATA[Soeleman, M. Arief]]>;
<![CDATA[Affandy, Affandy]]>;
<![CDATA[Marjuni, Aris]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4486
<![CDATA[The monkeypox outbreak has emerged as a pressing global health concern, as evidenced by the rising number of cases reported in various countries. This rare zoonotic disease, caused by the Monkeypox virus (MPXV) of the Poxviridae family, is commonly found in Africa. However, since 2022, cases have also spread to various countries, including Indonesia. The dermatological symptoms exhibited by affected individuals vary, with the potential for further transmission through contamination. Early and accurate detection of monkey pox disease is therefore essential for effective treatment. The present study aims to improve the classification of Monkey Pox using the modified Resnet50V2 model, trained using pre-training datasets namely ImageNet and HAM10000, where batch size and learning rate parameters were adjusted. The study achieved high accuracy in distinguishing monkeypox cases, with 98.43% accuracy for Resnet50V2 with pretrained ImageNet and 70.57% accuracy for Resnet50V2 with pretrained HAM10000. Future research will focus on refining these models, exploring hybrid approaches incorporating convolutional neural networks, this advancement contributes to the development of automated early diagnosis tools for monkeypox skin conditions, especially in resource-limited clinical settings where access to dermatology experts is limited.]]>
<![CDATA[Performance Optimization of ERD Designs Using Cost-Based Optimization for Large-Scale Query Processing ]]>
<![CDATA[Lubis, Juanda Hakim]]>;
<![CDATA[Handayani, Sri]]>;
<![CDATA[Mawengkang, Herman]]>;
<![CDATA[Napitupulu, Fajrul Malik Aminullah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4523
<![CDATA[The rapid growth of stored data, particularly on magnetic disks, is doubling annually for each department within a company, creating a pressing need for efficient database management. While database design is a fundamental step in establishing a high-performance system, it alone is insufficient to ensure optimal efficiency. Query optimization plays a critical role in improving data transaction speed, reducing query execution time, and enhancing overall system responsiveness. This study evaluates various relational database models under different data volumes to analyze their impact on query performance. Using the Cost-Based Optimizer method and access time measurements, we assess query costs and determine the factors influencing performance. The results indicate that among the three database models analyzed, ERD-3 consistently delivers superior performance, especially in handling complex queries. This is attributed to its modular structure, strategic indexing, and reduced full table scans, which collectively minimize query execution costs. Additionally, several key factors significantly affect query performance, including record count, attribute size, query complexity, primary and unique key usage, indexing strategies, order-by clauses, index sequences, and SQL function application. This research contributes to the field of database optimization by demonstrating that ERD structuring and cost-based query analysis significantly improve system efficiency in large-scale environments. These findings emphasize the necessity of a well-structured, scalable database model and efficient query processing techniques to accommodate large-scale data growth. The study’s conclusions provide a foundation for advanced optimization strategies, ensuring that modern database systems remain efficient and adaptable to evolving data demands.]]>
<![CDATA[Leveraging Convolutional Block Attention Module (Cbam) For Enhanced Performance In Mobilenetv3-Based Skin Cancer Classification ]]>
<![CDATA[Priambodo, Anas Rachmadi]]>;
<![CDATA[Fatichah, Chastine]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4546
<![CDATA[As the incidence of skin cancer continues to rise globally, effective automated classification methods become crucial for early detection and timely intervention. Lightweight neural networks such as MobileNetV3 offer promising solutions due to their minimal parameters, making them suitable for environment with low resource. This study aims to develop an automated multiclass skin cancer classification system by enhancing MobileNetV3 with the Convolutional Block Attention Module (CBAM). The primary goal is to achieve high classification accuracy without significantly increasing computational demands. We employed Bayesian optimization to automatically fine-tune model parameters and applied targeted data augmentation techniques to address class imbalance. CBAM was integrated to highlight diagnostically relevant regions within images. The proposed method was evaluated using the ISIC 2024 SLICE-3D dataset, which includes over 400,000 dermatoscopic images categorized into benign, basal cell carcinoma, melanoma, and squamous cell carcinoma classes. Preprocessing involved standardized resizing, normalization, and extensive geometric and photometric augmentations. Results demonstrated that our method achieved an accuracy of 98.97%, precision of 98.99%, recall of 98.97%, and an F1-score of 98.98%, surpassing previous state-of-the-art models by 1.86–6.52%. Remarkably, this improvement was achieved with minimal additional parameters due to the effective integration of CBAM. These results represent an advancement in automated medical image analysis, particularly for low resource settings, by combining lightweight CNNs with attention mechanisms and systematic hyperparameter exploration. ]]>
<![CDATA[Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers ]]>
<![CDATA[Setiyawan, Dillyana Tugas]]>;
<![CDATA[Berlilana, Berlilana]]>;
<![CDATA[Barkah, Azhari Shouni]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4577
<![CDATA[The variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services.]]>
<![CDATA[Improving Term Deposit Customer Prediction Using Support Vector Machine with SMOTE and Hyperparameter Tuning in Bank Marketing Campaigns ]]>
<![CDATA[Abidin, Dodo Zaenal]]>;
<![CDATA[Rosario , Maria]]>;
<![CDATA[Sadikin , Ali]]>;
<![CDATA[Nurhadi, Nurhadi]]>;
<![CDATA[Jasmir, Jasmir]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4585
<![CDATA[Identifying potential customers for term deposit products remains a challenge in the banking industry due to class imbalance in marketing datasets. This study proposes an integrated approach that combines Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) and hyperparameter tuning via GridSearchCV to enhance prediction performance. The dataset comprises 45,211 records containing demographic and campaign-related features. Preprocessing steps include categorical encoding, feature scaling, and SMOTE-based resampling. The optimized SVM model achieves an accuracy of 91% and an AUC of 0.96, outperforming the baseline model and demonstrating strong discriminatory ability, particularly for the minority class. This method improves the balance between precision and recall while reducing bias toward the majority class. The findings confirm the effectiveness of combining SMOTE and SVM for imbalanced classification tasks in the financial domain. These results contribute to the advancement of applied machine learning in informatics, particularly in developing robust decision support systems for data-driven banking strategies. Future work may extend this approach to diverse datasets and explore advanced resampling or ensemble techniques to improve model generalization.]]>
<![CDATA[Digital Forensic Chatbot Using DeepSeek LLM and NER for Automated Electronic Evidence Investigation ]]>
<![CDATA[Qonita, Nuurun Najmi]]>;
<![CDATA[Handayani, Maya Rini]]>;
<![CDATA[Umam, Khothibul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4593
<![CDATA[The growing complexity of cybercrime necessitates efficient and accurate digital forensic tools for analyzing electronic evidence. This research presents an intelligent digital forensic chatbot powered by DeepSeek Large Language Model (LLM) and Named Entity Recognition (NER), designed to automate the analysis of various digital evidence, including system logs, emails, and image metadata. The chatbot is deployed on the Telegram platform, providing real-time interaction with investigators. The metric results show that the chatbot achieves a precision of 83.52%, a recall of 88.03%, and an F1-score of 85.71%. These results demonstrate the chatbot's effectiveness in accurately detecting forensic entities, significantly improving investigation efficiency. This study contributes to digital forensics by integrating LLM and NER for enhanced evidence analysis, offering a scalable and adaptive solution for automated cybercrime investigations. Future research may explore integrating anomaly detection and blockchain-based evidence integrity.]]>
<![CDATA[Optimal Phase Selection Of Single-Phase Appliances In Buildings Using String-Coded Genetic Algorithm ]]>
<![CDATA[Daratha, Novalio]]>;
<![CDATA[Vatresia, Arie]]>;
<![CDATA[Santosa, Hendy]]>;
<![CDATA[Agustian, Indra]]>;
<![CDATA[Suryadi, Dedi]]>;
<![CDATA[Gupta, Neeraj]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4603
<![CDATA[Phase imbalance in buildings, primarily caused by single-phase loads and generation, leads to increased neutral current, voltage imbalance, reduced energy efficiency, and potential equipment damage. To address these challenges, an optimal phase selection method is proposed for single-phase loads and generation. This method integrates integer programming with a string-coded genetic algorithm (GA). The GA employs string encoding to represent phase connections. Initially, a Mixed Integer Programming (MIP) solver identifies an initial solution, which is subsequently transformed into a string to initialize the GA’s genes. Subsequently, the GA executes standard operations such as mutation, crossover, evaluation, and selection. Case studies demonstrate the efficacy of this method in achieving substantial load balancing. Notably, the identification of multiple solutions with identical objective function values renders this approach suitable for smart buildings equipped with energy management systems that participate in ancillary services between low-voltage and medium-voltage networks. This research pertains to the domains of computer science, power engineering, and energy informatics.]]>
<![CDATA[Identifying Academic Excellence: Fuzzy Subtractive Clustering of Student Learning Outcomes ]]>
<![CDATA[Wibowo, Muhammad Bagas Satrio]]>;
<![CDATA[Hindrayani, Kartika Maulida]]>;
<![CDATA[Trimono, Trimono]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4614
<![CDATA[Education forms a vital foundation for a nation's future. In this digital era, while the use of Information and Communication Technology (ICT) in education is increasing, it brings increasingly complex challenges in education data management and analysis. The growing number of students each year results in a large volume of data, which would be difficult to manage if still relying on manual methods. Manual approaches are inefficient, time-consuming, prone to inconsistencies and human error, especially when identifying outstanding students in large and complex data. This research aims to implement a clustering system to group outstanding students at XYZ elementary school using the Fuzzy Subtractive Clustering (FSC) method. FSC was chosen for its ability to identify data groups based on the density of data points. FSC involves several important parameters, including radius, squash factor, acceptance ratio, and rejection ratio. Added variabel of social and spiritual values aims to enhance grouping quality by offering a broader perspective on students' character, attitudes, and social interactions. Parameter exploration shows an increase in the silhouette score from 0.20–0.45 to 0.45-0.57 and variable addition spiritual and social values, which indicates clearer cluster separation and provides better insights. The best parameters results were achieved with radius 0.3, accept ratio 0.5, reject ratio 0.04, and squash factor 1.25, resulting in a Silhouette Score of 0.57 and forming 5 student groups. Cluster results can guide special mentoring for students with low academic, spiritual, and social values, and support personalized learning programs based on each cluster’s characteristics.]]>
