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Journal of Technology Informatics and Engineering
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29619068     EISSN : 29618215     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Power Engineering Telecommunication Engineering Computer Engineering Control and Computer Systems Electronics Information technology Informatics Data and Software engineering Biomedical Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 161 Documents
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Artificial Intelligence in Education Management: A Systematic Review of Decision Support Systems for Inclusive Education Jamaludin, Haris
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.430

Abstract

This study examines the integration of artificial intelligence (AI) into education management systems, focusing on decision-support mechanisms for inclusive education. Through a systematic literature review of publications from 2019 to 2024, this research analyzes how AI technologies transform educational management practices and inform strategic decision-making. The findings reveal that AI-driven management systems significantly enhance resource allocation, personalized learning path creation, and inclusive education monitoring through advanced data analytics and predictive modeling. However, implementation challenges include data integration complexities, staff training requirements, and ethical considerations in algorithmic decision-making. The study identifies critical success factors for AI adoption in educational management, including leadership commitment, technological infrastructure, and stakeholder engagement. This research contributes to management science by providing a framework for AI implementation in educational institutions. It offers practical insights for education managers, policymakers, and technology developers seeking to leverage AI for inclusive education management.
A Hybrid Noise Reduction And Normalization Framework For Improving Multimodal Sensor Data Quality In Real-Time Systems Ram, Kim Sa; Hoon, Park Ji; Yeon, Hong Jae
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.440

Abstract

Multimodal sensor data, integrating signals such as RGB, LiDAR, and IMU, plays a pivotal role in enabling intelligent decision-making in real-time Internet of Things (IoT) systems. However, these data streams are inherently prone to complex noise patterns, cross-sensor inconsistencies, and scaling disparities that conventional preprocessing techniques often fail to address comprehensively. This paper presents a hybrid data preprocessing framework that unifies advanced denoising and adaptive normalization in a single, context-aware pipeline. The framework leverages wavelet-based denoising for high-frequency noise suppression, Kalman filtering for dynamic state estimation, and a real-time adaptive normalization mechanism that calibrates data scaling based on temporal and environmental contexts. Evaluations on synchronized multimodal IoT datasets comprising RGB, LiDAR, and IMU recordings under low-light, high-noise, and adverse-weather conditions (≈ 18,000 aligned samples; 30 Hz, 10 Hz, 100 Hz) show significant performance gains. Results indicate a 30.4% RMSE reduction (p < 0.05), 33% faster convergence, and only 34% computational overhead, while maintaining real-time feasibility with a 41 ms latency per frame. These findings confirm that combining complementary denoising paradigms with adaptive, context-driven normalization enhances signal fidelity and responsiveness in dynamic sensing environments. This contribution presents a reproducible, statistically validated hybrid preprocessing framework for enhancing the quality of multimodal sensor data, enabling more reliable deployments in industrial automation, environmental monitoring, and intelligent transport systems.
The Role of Social Media as a Micro-Ecosystem in Supporting Community-Based E-Learning Platforms: A Systematic Literature Review Agustin, Naila; Rahayu, Septi
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.445

Abstract

This study explores the role of social media as a micro-ecosystem that supports community-based E-learning, adopting a Systematic Literature Review (SLR) approach guided by the PRISMA protocol. From 450 screened publications, 80 relevant studies were analysed using both qualitative thematic synthesis and descriptive meta-analysis. The findings reveal that social media platforms, such as WhatsApp, Facebook, YouTube, and Discord, enhance collaboration, engagement, and learning motivation, with average improvements of about 40% in learner participation and 98% in message openness, based on aggregated quantitative evidence from prior research. The review also identifies significant challenges, including digital distraction, privacy risks, and limited digital literacy, which can reduce the effectiveness of social-media-based learning. By integrating adaptive learning algorithms, AI-driven analytics, and privacy-by-design principles, the study conceptualises social media as an intelligent, ethically grounded, and sustainable learning micro-ecosystem aligned with informatics and systems-engineering perspectives.
Zero-Shot Learning For Multilingual Document Classification In Low-Resource Languages Orinos, Nasios; Onola, Quedevo; Chistoff, Ong Ben
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.446

Abstract

Document classification in low-resource languages remains a critical challenge due to the scarcity of annotated datasets, language-specific resources, and linguistic tools. This study investigates the effectiveness of zero-shot learning (ZSL) for multilingual document classification, with a specific focus on low-resource Southeast Asian languages: Javanese, Sundanese, and Malay. We adopt a zero-shot cross-lingual transfer approach, using English-labeled data as the source domain and evaluating on unseen target-language documents without any supervised fine-tuning. Specifically, we employ two state-of-the-art multilingual transformer models, XLM-RoBERTa (XLM-R) and Multilingual T5 (mT5), to evaluate their ability to generalize across linguistically distant languages. Experimental results show that XLM-R achieves higher average accuracy (≈78%) and F1 Score (≈0.76) than mT5 (≈74% accuracy, 0.72 F1), demonstrating stronger transferability and stability. Both models exhibit efficient inference speed and manageable computational costs, indicating potential for deployment in resource-constrained environments. The findings introduce an early benchmark for zero-shot multilingual document classification in Southeast Asian languages and highlight the feasibility of inclusive NLP systems that bridge the data gap for underrepresented linguistic communities.
Adaptive Control of Autonomous Mobile Robots Using Fuzzy Logic Based PID Optimization Almeida, Sofia; Chen, Michael
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.454

Abstract

Autonomous mobile robots require precise navigation and stability in dynamic environments, where traditional control methods often fail to balance accuracy, responsiveness, and robustness. This study proposes an adaptive fuzzy–PID control framework to optimize real-time trajectory tracking and disturbance rejection. The approach integrates a fuzzy inference system with adaptive proportional integral–derivative (PID) gain tuning, enabling continuous adjustment of control parameters based on instantaneous tracking error and error rate. The methodology combines MATLAB/Simulink and ROS Gazebo simulations with physical experiments on a differential-drive mobile robot equipped with LiDAR, inertial sensors, and high-resolution wheel encoders. Results demonstrate that the adaptive fuzzy–PID controller reduced overshoot by 42%, shortened settling time by 35%, and maintained a steady-state lateral error below 1 cm and heading deviation under 0.5°, outperforming classical PID and conventional fuzzy-PID schemes. These findings confirm robust adaptation to nonlinear dynamics and unexpected disturbances without significant computational overhead. The proposed framework emphasizes interpretability and practical applicability, providing insights for multi-robot coordination, self-driving vehicles, and industrial or service robotics where reliability and safety are critical.
Quantum-Inspired Optimization for High-Dimensional Data Classification in Healthcare Analytics Sugimoto, Hanae; Morishita, Kaito
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.451

Abstract

High-dimensional medical datasets pose a persistent challenge for artificial intelligence because traditional classification algorithms often incur escalating computational costs and reduced predictive accuracy. As healthcare systems generate increasingly complex clinical records, imaging outputs, and genomic profiles, scalable analytic methods that balance precision and efficiency are critical. This study proposes a Quantum-Inspired Optimization (QIO) framework for efficient and accurate classification of high-dimensional healthcare data. Leveraging the exploratory power of variational quantum algorithms, specifically techniques analogous to the Quantum Approximate Optimization Algorithm, the framework integrates quantum-style search strategies with classical computation to achieve global optimization and numerical stability. Publicly available medical datasets with hundreds of features were used to evaluate the approach. Classification models were trained and tested across varying feature dimensionalities, and performance was assessed using accuracy, runtime, and scalability metrics. Empirical results demonstrate that QIO achieves up to 95.4% classification accuracy and reduces computational time by 40% compared with state-of-the-art classical baselines. The method demonstrates stable convergence and clear decision boundaries even as feature dimensionality grows, highlighting its resilience to the curse of dimensionality. These results indicate that QIO can enable fast and reliable healthcare analytics in data-rich clinical environments. Future research may examine domain-specific adaptations, real-time deployment, and integration with emerging quantum hardware to enhance the impact of quantum-inspired artificial intelligence further.
Optimizing High-Frequency Antenna Design through Genetic Algorithm-Based Electromagnetic Simulation Hart, Evelyn; King, Oliver
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.452

Abstract

The emergence of sixth-generation (6G) wireless networks demands broadband antennas capable of ultra-high data throughput and seamless global connectivity. This study presents a genetic-algorithm (GA) optimization framework to enhance antenna performance, focusing on patch dimensions, ground-plane size, and feed position. Full-wave electromagnetic simulations were performed in CST Microwave Studio and ANSYS HFSS, employing defined mesh sizes, solver types, and boundary conditions to ensure accurate evaluation. The GA-based optimization achieved an impedance bandwidth of 3.2–6.1 GHz, a peak gain improvement of 2.8 dB, and radiation efficiency exceeding 92%, outperforming conventional gradient-based tuning. The optimized antenna exhibited stable S-parameters and an omnidirectional radiation pattern across the target spectrum, confirming reliable operation at high frequencies. This approach highlights the advantages of evolutionary algorithms in enabling efficient, manufacturable, and high-performance broadband antenna designs for next-generation wireless systems. Beyond immediate 6G applications, the methodology can be extended to millimeter-wave and terahertz antennas, supporting continued innovation in ultra-reliable, high-capacity wireless communications.
3D Medical Image Reconstruction through Transformer-Based Neural Networks: A Comparative Study Tan, Wei Ling; Menon, Arjun
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.453

Abstract

Three-dimensional reconstruction of CT and MRI images remains a persistent challenge in medical imaging, where clinicians require high‐fidelity volumes that preserve subtle anatomical details while remaining computationally efficient. This study evaluates a transformer-based neural network against a conventional convolutional neural network (CNN) baseline to determine which architecture delivers superior reconstruction accuracy for clinical use. A standard deep learning pipeline was constructed, which included data curation, intensity normalization, and augmentation, prior to training the models. The experimental comparison studied two representative architectures, a 3D U-Net that served as the CNN benchmark, and a 3D Swin Transform, that served as the attention approach. The quantitative analysis showed that the transformer produced a higher Peak Signal-to-Noise-Ratio (35.8 dB vs 33.1 dB), better Structural Similarity Index Measure (0.942 vs 0.911), and better Dice coefficient (0.91 vs 0.87) with little differences with respect to inference time per volume. The visual analysis showed sharper cortical folds and clearer lesion edges, which radiologists linked with higher diagnostic confidence. The transformer’s ability to model global spatial dependencies and reduce noise artifacts facilitates accurate and clinically pertinent reconstructions. This study shows that transformer models can be computationally efficient but more precise than CNN alternatives, which support their implementation in hospital Picture Archiving and Communication Systems (PACS) and within future real time patient diagnostics workflows. Taken together, these findings support the collective efforts of engineers and healthcare providers to leverage future algorithmic improvements that can enhance patient care and the safety of imaging.
Adaptive Fuzzy Logic Integration for Optimizing Decision Support Systems under Data Uncertainty Kuznetsova, Elena; Nkosi, Mbali
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.456

Abstract

Decision Support Systems (DSS) become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodology may be strong at handling vagueness, but are unable to adapt their behavior to different data distributions on their own. This paper introduces an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests on uncertainty-generated datasets demonstrate that the adaptive model achieves a mean accuracy gain of 21.4% and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its responsiveness and robustness under uncertainty. The framework provides a self-tuning fuzzy decision model that transforms static inference structures into dynamic, evolving decision engines, establishing a foundation for next-generation smart DSS for real-time optimization.
User-Centered Mobile Navigation: Evaluating Local Usability for Improved UX Petrova, Sofia; Watanabe, Ken
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.457

Abstract

Mobile navigation interfaces continue to be plagued by the key usability problem, particularly where widely accepted design traditions fail to address the specific local user population requirements and mental models. This research addresses this significant limitation by using a thorough user-focused measurement framework to search for and investigate context-dependent navigation problems in mobile applications. The research employed a multi-method qualitative approach, using in-depth questionnaires, semi-structured interviews, and laboratory-based usability testing of 15 participants on an interactive Figma prototype that simulated real-world navigation tasks. Our analysis step by step revealed significant navigation issues within the local context, quantified using a 73% task completion rate and a 2.8 participant error average for the core navigation tasks. The most significant usability issues were ambiguous iconography, inconsistent application of platform design patterns, and insufficient system feedback mechanisms. The results conclusively demonstrate that localized usability testing is not merely beneficial but necessary while creating genuinely good and accessible mobile experiences. The study provides a replicable, practical context-aware evaluation approach and tangible, right-now applicable design recommendations, for example, the need to enhance icons with descriptive text labels and use instant visual feedback mechanisms. The study provides developers and designers tangible take-away recommendations for significantly enhancing navigation user experience without trading off methodological availability and implementation convenience in different development environments.

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