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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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Predicting and Inspecting Food Contamination Using AI based Hyperspectral Imaging Sudha, S.; Nafeeza, S.
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | 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.v4i2.266

Abstract

Growing consumer demands and intricate supply networks are making it more difficult for the global food industry to maintain high standards of quality and ensure food safety. Conventional inspection techniques sometimes take a lot of time, cause damage, and are inaccurate enough to miss contaminants or quality problems early. These drawbacks emphasize how sophisticated, effective, and non-invasive technology is required for food quality monitoring. This effort aims to investigate the use of Hyperspectral Imaging (HSI) in conjunction with Artificial Intelligence (AI) for food contamination inspection and prediction. Food's chemical and physical characteristics that are undetectable to the human eye can be revealed by hyperspectral imaging, which takes pictures at a variety of wavelengths. The findings show that AI-based HSI offers notable advantages over traditional techniques in terms of quick, accurate, and non-destructive examination. It makes early contamination detection possible and aids in preserving food quality throughout the supply chain. By reducing waste, guaranteeing product authenticity, and boosting customer trust in food items, our effort helps worldwide food safety and advance the development of smarter food inspection systems.
Smart Healthcare: Harnessing AI for Early prediction of Neurodegenerative disease Harrisha, M.; Monikasree, J.; Swathi, J.; Karthika, D.
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | 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.v4i2.269

Abstract

Neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and Amyotrophic Lateral Sclerosis (ALS) are progressive conditions that result in the deterioration of neuronal function. Early diagnosis remains a significant challenge due to the subtle onset of symptoms and the lack of accessible diagnostic tools, particularly in low-resource settings. These challenges often lead to delayed interventions and poor patient outcomes. This research proposes an innovative healthcare solution for the early prediction and monitoring of neurodegenerative diseases, utilizing artificial intelligence (AI) and wearable technology. The proposed system integrates an AI-powered mobile application that analyzes patient medical history using secure Aadhaar-based authentication. The model utilizes Recurrent Neural Networks (RNNs) and transfer learning to detect early-stage neurodegeneration. Although the system is currently in a conceptual stage, preliminary testing was performed using simulated patient data to verify workflow functionality. The complete model is designed to be evaluated using benchmark datasets, such as ADNI and MIMIC-III, with metrics including accuracy, F1-score, and ROC-AUC. The wearable device continuously monitors vital signs and provides real-time alerts for patients and guardians. This comprehensive framework addresses gaps in early diagnosis, enhances accessibility, and supports proactive care for underserved communities.
The Architecture of Intellegent Transportation System based on Sensor Monitoring (Implementation in Jakarta Area) Handoko, Melyani; Mubarok, Husni; Shaura, Rizkiana Karmelia; Lesmana, Hendra; Widyastuti, Reni; Swastika, Rahayu; Haryanto, Wawan; Hartini, Dorit
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | 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.v4i2.357

Abstract

Urban congestion and traffic inefficiencies pose significant challenges for developing cities such as Jakarta. Conventional traffic management systems often lack responsiveness and integration, leading to time delays, fuel wastage, and safety hazards. This study proposes the architecture of an Intelligent Transportation System (ITS) based on sensor monitoring as a solution to improve vehicle flow, surveillance, and timeliness within urban transportation networks. The objective is to develop a sensor-based ITS framework capable of real-time monitoring and decision-making through the integration of motion, ultrasonic, PIR (Passive Infrared), and speed sensors. The proposed system was simulated in the Jakarta metropolitan context, focusing on its feasibility and performance under dense traffic scenarios. The results demonstrate that the incorporation of four monitoring technologies—RFID, embedded sensors, IP address tracking, and QR barcode scanning—can significantly enhance the efficiency of traffic control. The framework enables vehicle surveillance, automated violation ticketing, and optimized travel time estimation. It also proposes a comprehensive four-layer surveillance system encompassing traffic, vehicle, passenger, and driver monitoring. This study contributes to the development of smart city infrastructure by offering a scalable ITS model that minimizes congestion, supports automated law enforcement, and enhances public transportation accessibility through digital integration. The findings serve as a baseline for future implementation and technological expansion of ITS in other congested urban regions. 
Evaluating Digital Transformation within Integration Limitations using Desk-Based Analytical Case Study Nugroho, Setiawan Arkha Ade; Wibowo, Agus
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | 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.v4i2.365

Abstract

Digital transformation in developing regions frequently encounters challenges, including inadequate infrastructure, limited system integration, and uneven digital literacy. These constraints are especially significant in local government institutions where policy readiness and system interoperability remain low. This study evaluates the effectiveness of hybrid records management as a strategy for facilitating digital transformation, despite limitations in integration. A desk-based analytical case study was conducted on the M’mbelwa District Council in Malawi, using a mixed-method exploratory design. Qualitative analysis applied the TOE Framework, UTAUT/TAM, and the Records Continuum Model, while quantitative validation used the DeLone and McLean Information Systems Success Model. The findings show that, despite the absence of formal IoT policies, the council effectively utilized basic devices, such as computers, mobile phones, and notebooks, for daily data recording and archival management. Staff capability was measured at 50 percent intermediate and 30 percent advanced, with 100 percent daily usage. Security measures included passcode protection and regular backups. Quality scores reached 4.5 out of 5 for information relevance and 4.3 out of 5 for user satisfaction, while efficiency improved by about 25 percent. The archives lifecycle, covering creation, capture, organization, and transfer to the National Archives, was implemented effectively, although disposal procedures were absent. This study demonstrates that hybrid records management can bridge the digital divide and sustain public services in low-resource contexts. The results make theoretical contributions to the digital transformation literature and provide practical guidance for policymakers facing similar infrastructural and policy challenges.
Computational Fluid Dynamics (CFD) Optimization in Smart Factories: AI-Based Predictive Modelling Ibrahim, Said Maulana; Najmi, M. Ikhwan
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | 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.v4i1.264

Abstract

In the era of Industry 4.0, optimizing fluid flow systems in smart factories is essential to improve energy efficiency and operational stability. Traditional Computational Fluid Dynamics (CFD) simulations provide accurate fluid flow analysis but require extensive computational resources and long processing times, making real-time applications challenging. To address this limitation, this study aims to develop an AI-based predictive model for CFD simulations, utilizing Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) to accelerate the estimation of fluid flow characteristics in industrial environments. The research methodology involves generating CFD simulation datasets, preprocessing data, and training AI models to predict key fluid parameters such as pressure, velocity, and temperature. The evaluation results show that CNN achieves a Mean Squared Error (MSE) of 0.0025 and a Root Mean Squared Error (RMSE) of 0.05, outperforming XGBoost, which records an MSE of 0.0030 and an RMSE of 0.055. Moreover, CNN predicts fluid dynamics in just 15.2 seconds, while XGBoost achieves results in 10.5 seconds, compared to the 1200.5 seconds required by traditional CFD simulations. These findings highlight the potential of AI in reducing computation time by over 98%, making real-time fluid flow analysis feasible in industrial settings. This study contributes to the advancement of AI-integrated CFD modeling, demonstrating that AI can significantly enhance the efficiency of fluid dynamics analysis without compromising accuracy. Future research should focus on expanding AI models to handle more complex flow conditions and integrating AI with smart factory design tools for real-time optimization
Transforming Fraud Detection in Banking with Explainable AI : Enhancing Transparency and  Trust Sivaranjani, S.; Hassan S. , Noorul
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | 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.v4i2.267

Abstract

As financial fraud grows in complexity, banks are turning to artificial intelligence (AI) to identify and avert fraudulent actions. Nevertheless, the conventional black-box AI models often lack clarity, leading to issues related to trust, adherence to regulations, and customer assurance. This paper investigates the function of Explainable AI (XAI) in revolutionizing fraud detection in the banking industry by connecting algorithmic clarity with stakeholder confidence. We analyze how XAI improves fraud detection systems by making AI-generated decisions more understandable for regulators, auditors, and customers while preserving high levels of detection accuracy. By promoting transparency, accountability, and trust, XAI is transforming the financial sector’s strategy for addressing fraud. Traditional rule-based systems are no longer sufficient due to the growing complexity of hackers, which is why banks are adopting AI-driven and machine learning solutions. However, many sophisticated models' opacity presents serious difficulties for risk management and regulatory compliance. This gap is filled by explainable AI, which offers insights into decision-making processes and produces outputs that are easy to understand without sacrificing predictive ability. Integrating XAI into fraud detection systems becomes crucial as customers want more comfort regarding the handling of their financial data and regulatory bodies demand greater responsibility. This report emphasizes how important XAI is to improving operational resilience and bolstering consumer and bank trust.
Virtual Reality in Adaptive Learning: Identifying Learning Styles and Integration in Educational Apps Divyadharshini, P.; Princi, R.; Subashini, G.; Moohambgai, B.
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | 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.v4i2.268

Abstract

The increasing demand for personalized education in digital environments has highlighted the limitations of traditional, static learning methods. This study addresses the challenge of accommodating diverse learning preferences and emotional responses by leveraging Virtual Reality (VR) to create immersive, adaptive learning systems. The research focuses on designing a VR-based educational framework that integrates artificial intelligence to personalize instruction, monitor emotional states, and support ethical assessment. A primary goal is to identify how students' learning styles and emotional conditions influence their experience in VR, and how educational systems can adapt in real time to these factors. Key components of the proposed system include customizable AI tutors that resemble familiar figures to boost learner motivation, real-time emotion detection during virtual exams, and cognitive load management through adaptive content delivery. Methods involve tracking emotions through facial analysis, monitoring gaze, and providing physiological feedback to dynamically adjust test environments. The findings suggest that while VR enhances engagement and satisfaction, excessive sensory input can lead to cognitive strain, underscoring the importance of balanced immersion. The study contributes a novel, emotion-driven VR framework that combines personalization, gamification, and ethical monitoring. This integrated approach provides a foundation for inclusive, engaging, and effective virtual learning systems that align with individual learner needs and support academic integrity.
Semantic Role Labeling in Neural Machine Translation Addressing Polysemy and Ambiguity Challenges Qin, Yan
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | 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.v4i1.274

Abstract

The persistent challenges of polysemy and ambiguity continue to hinder the semantic accuracy of Neural Machine Translation (NMT), particularly in language pairs with distinct syntactic structures. While transformer-based models such as BERT and GPT have achieved notable progress in capturing contextual word meanings, they still fall short in understanding explicit semantic roles. This study aims to address this limitation by integrating Semantic Role Labeling (SRL) into a Transformer-based NMT framework to enhance semantic comprehension and reduce translation errors. Using a parallel corpus of 100,000 English-Indonesian and English-Japanese sentence pairs, the proposed SRL-enhanced NMT model was trained and evaluated against a baseline Transformer NMT. The integration of SRL enabled the model to annotate semantic roles, such as agent, patient, and instrument, which were fused with encoder representations through semantic-aware attention mechanisms. Experimental results demonstrate that the SRL-integrated model significantly outperformed the standard NMT model, improving BLEU scores by 6.2 points (from 32.5 to 38.7), METEOR scores by 6.3 points (from 58.5 to 64.8), and reducing the TER by 5.8 points (from 45.1 to 39.3). These results were statistically validated using a paired t-test (p < 0.05). Furthermore, qualitative analyses confirmed SRL's effectiveness in resolving lexical ambiguities and syntactic uncertainties. Although SRL integration increased inference time by 12%, the performance trade-off was deemed acceptable for applications requiring higher semantic fidelity. The novelty of this research lies in the architectural fusion of SRL with transformer-based attention layers in NMT, a domain seldom explored in prior studies. Moreover, the model demonstrates robust performance across linguistically divergent language pairs, suggesting its broader applicability. This work contributes to the advancement of semantically aware translation systems and paves the way for future research in unsupervised SRL integration and multilingual scalability.
Lightweight Deepfake Detection on Mobile Devices Using Attention-Enhanced MobileNet and Frequency Domain Analysis Amen, Mohammad; Ranam, Mohammed Lauwl
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | 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.v4i1.275

Abstract

The rapid advancement of deepfake technology has raised significant concerns regarding misinformation, privacy breaches, and digital fraud. Existing deepfake detection models, particularly those based on deep learning, often require high computational resources, making them unsuitable for real-time applications on mobile devices. This study aims to develop a lightweight deepfake detection model that enhances accuracy while maintaining computational efficiency. To achieve this, we propose a hybrid approach that integrates Fast Fourier Transform (FFT), MobileNet, and an Attention mechanism. The FFT component enables frequency-domain analysis to detect subtle deepfake artifacts, while MobileNet provides a lightweight convolutional backbone, and the Attention layer enhances feature extraction. The proposed model was evaluated on a benchmark deepfake dataset, and the results demonstrated its superior performance compared to the standard MobileNet model. Specifically, the model achieved an accuracy of 94.2%, an F1-score of 93.8%, and a computational efficiency improvement of 27.5% in comparison to conventional CNN-based approaches. These findings indicate that the integration of FFT and Attention mechanisms significantly enhances the model's capability to distinguish real and manipulated media while reducing computational overhead. The contribution of this study lies in presenting a deepfake detection model that balances accuracy and efficiency, making it suitable for deployment in mobile and resource-constrained environments. Future research should explore further optimization for energy efficiency, the adoption of lightweight Transformer architectures, and extensive testing on diverse datasets to improve robustness against real-world variations.
Transfer Learning Approach for Sentiment Analysis in Low-Resource Austronesian Languages Using Multilingual BERT Hao, Li Wen; Liu, Robert Kuan
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | 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.v4i1.276

Abstract

Sentiment analysis for low-resource languages, particularly Austronesian languages, remains challenging due to the limited availability of annotated datasets. Traditional approaches often struggle to achieve high accuracy, necessitating strategies like cross-lingual transfer and data augmentation. While multilingual models such as mBERT offer promising results, their performance heavily depends on fine-tuning techniques. This study aims to improve sentiment analysis for Austronesian languages by fine-tuning mBERT with augmented training data. The proposed method leverages cross-lingual transfer learning to enhance model robustness, addressing the scarcity of labeled data. Experiments were conducted using a dataset enriched with augmentation techniques such as back-translation and synonym replacement. The fine-tuned mBERT model achieved an accuracy of 92%, outperforming XLM-RoBERTa at 91.41%, while mT5 obtained the highest accuracy at 99.61%. Improvements in precision, recall, and F1-score further validated the model’s effectiveness in capturing subtle sentiment variations. These findings demonstrate that combining data augmentation and cross-lingual strategies significantly enhances sentiment classification for underrepresented languages. This study contributes to the development of scalable Natural Language Processing (NLP) models for Austronesian languages. Future research should explore larger and more diverse datasets, optimize real-time implementations, and extend the approach to tasks such as Named Entity Recognition (NER) and machine translation. The promising results underscore the importance of integrating robust transfer learning techniques with comprehensive data augmentation to overcome challenges in resource-limited NLP scenarios

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