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Jurnal Teknik Informatika (JUTIF)
Published by Universitas Jenderal Soedirman
ISSN : 27233863     EISSN : 27233871     DOI : https://doi.org/10.20884/1.jutif.Year.Vol.No.ID
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 962 Documents
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Multi-Class Brain Tumor Segmentation and Classification in MRI Using a U-Net and Machine Learning Model Hendrik, Jackri; Pribadi, Octara; Hendri, Hendri; Hoki, Leony; Tarigan, Feriani Astuti; Wijaya, Edi; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5369

Abstract

Brain tumor diagnosis remains a critical challenge in medical imaging, as accurate classification and precise localization are essential for effective treatment planning. Traditional diagnostic approaches often rely on manual interpretation of MRI scans, which can be time-consuming, subjective, and prone to variability across radiologists. To address this limitation, this study proposes a two-stage framework that integrates machine learning (ML) based classifiers for tumor type recognition and a U-Net architecture for tumor segmentation. The classifier was trained to distinguish four tumor categories: glioma, meningioma, pituitary, and no tumor, while the U-Net model was employed to delineate tumor regions at the pixel level, enabling volumetric assessment. The novelty of this research lies in its dual focus that combines classification and segmentation within a single framework, which enhances clinical applicability by offering both diagnostic and spatial insights. Experimental results demonstrated that among the evaluated classifiers, XGBoost achieved the highest accuracy of 86 percent, surpassing other models such as Random Forest, SVC, and Logistic Regression, while the U-Net model delivered consistent segmentation performance across tumor types. These findings highlight the potential of hybrid ML and deep learning solutions to improve reliability, efficiency, and objectivity in brain tumor analysis. In real-world practice, the proposed framework can serve as a valuable decision-support tool, assisting radiologists in early detection, reducing diagnostic workload, and supporting personalized treatment strategies.
Decision Support System for Selecting Outstanding Religious Counselors in Jambi Province Using Analytical Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution Suryani, Suryani; Zaenal Abidin, Dodo; Purnama, Benni; Gunardi, Gunardi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5385

Abstract

Religious counselors play an essential role in fostering religious moderation, strengthening community cohesion, and promoting social harmony. However, the evaluation of their performance remains largely manual, leading to subjectivity, inconsistency, and limited accountability. This study develops a web-based Decision Support System that integrates the Analytical Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution to enhance objectivity, transparency, and data-driven evaluation. The Analytical Hierarchy Process was applied to determine the importance of five criteria—portfolio, scientific paper, program video, presentation or interview, and absenteeism—through expert pairwise comparisons. The Technique for Order Preference by Similarity to Ideal Solution was then used to rank twenty-four religious counselors from the Regional Office of the Ministry of Religious Affairs in Jambi Province. The results show that portfolio (47.4%) and presentation or interview (24.4%) were the most influential criteria, while the others served as complementary factors. Counselors with comprehensive documentation and strong communication skills consistently ranked higher, validating the system’s analytical reliability. This study’s novelty lies in applying a multi-criteria decision-making framework within the religious sector, directly aligned with the 2024 Technical Guidelines for the Islamic Religious Counselor Award (Keputusan Dirjen Bimas Islam No. 352/2024). Furthermore, this research supports the Ministry of Religious Affairs’ Eight Priority Transformation Programs (Asta Protas), particularly in digitalizing governance and promoting transparent, accountable, and data-driven management. From an informatics perspective, this system demonstrates the effective implementation of decision-support algorithms in a web-based environment, highlighting the contribution of information technology to evidence-based performance evaluation.

Page 97 of 97 | Total Record : 962

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