Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Articles
1,242 Documents
Hand Image-Based BISINDO Alphabet Classification Utilizing Convolutional Neural Network on Android Application
Saniyyah Intan Salsabiila;
Nataniel Dengen;
Joan Angelina Widians
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman
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DOI: 10.52436/1.jutif.2026.7.4.6294
Communication barriers between people with hearing impairments and the general public remain a significant challenge in everyday interactions. BISINDO is a widely used sign language in Indonesia’s deaf community; however, public understanding of sign language remains limited. This study aims to develop a hand image-based BISINDO alphabet classification system using a Convolutional Neural Network model and implement the trained model in an Android application. The public dataset used consists of 10,686 BISINDO alphabet letter images representing 26 classes from A to Z, obtained from Kaggle. All images were preprocessed, including resizing, normalization, and data augmentation to increase the diversity of the training data. The model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the model achieved a validation accuracy of 94.21%, precision of 94.38%, recall of 94.20%, and F1-score of 94.21%. The trained model was then integrated into an Android application named IsyaratKu using the Flutter framework and the Dart programming language. The implementation results showed that the application successfully recognized the letters of the BISINDO alphabet through camera and gallery input and performed effectively on Android devices. These results indicate that the CNN can classify the 26 BISINDO alphabet classes with validation accuracy above 94% and can be deployed in an Android application for offline alphabet classification.
Clustering Evaluation of Upper Air Thermodynamic Patterns Using K-Means Centroid Optimization with GA, PSO, and GWO Algorithms
Ricky Aurelius Nurtanto Diaz;
Ni Luh Gede Pivin Suwirmayanti;
Emy Setyaningsih;
Agus Yarcana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman
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DOI: 10.52436/1.jutif.2026.7.4.6375
Operationally used early warning systems for significant weather generally rely on conventional weighting-scoring schemes, namely, fixed thresholds set based on empirical experience or expert meteorological consensus. The fundamental limitation of conventional threshold-based methods and supervised classification models lies in their reliance on labeled data, while extreme weather events are naturally rare and imbalanced in historical station observation records. Such approaches are prone to generating false alarms. Therefore, a multilayered unsupervised learning approach is needed to objectively classify upper-air thermodynamic patterns without relying on subjective extreme-event labels. Although widely used, the K-Means algorithm has a fundamental weakness, namely its high sensitivity to the initial centroid determination. This study applies hybrid metaheuristic techniques with K-Means, namely GA-KMeans, PSO-KMeans, and GWO-KMeans, within a consistent evaluation framework, using the same cluster validity index, and applied to the operational meteorological domain. The test results show that the best fitness values are produced by the PSO-KMeans model with a WCSS value of 250.6012, followed by GWO-KMeans with a WCSS value of 304.7890, and finally the GA-KMeans model with a WCSS value of 330.0701. The third model used also consistently produces higher Silhouette Score values compared to the classical K-Means baseline and shows that metaheuristic hybridization is proven to be effective in improving the quality of cluster structures. Specifically within the field of computer science, the results demonstrate that employing appropriate cluster center optimization techniques can improve both clustering quality and resource efficiency when grouping various types of data.