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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 10 Documents
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Search results for , issue "Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering" : 10 Documents clear
Optimization of Inclusive Education Through the Implementation of Artificial Intelligence: Opportunities and Challenges Jamaludin, Haris; Hartono, Budi; Kuncoro, Arsito Ari
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.v4i2.219

Abstract

Inclusive education is critical to ensuring equitable learning opportunities for all students, regardless of their abilities or backgrounds. This study aims to analyze the optimization of inclusive education by implementing artificial intelligence (AI), focusing on identifying the opportunities and challenges that arise. A systematic literature review was conducted as the research method, referencing five journals related to the application of AI in education and other relevant sectors. The findings reveal that AI has significant potential to enhance the quality of inclusive education by enabling personalized learning materials, real-time student data analysis, and improved teacher-student interactions. These advancements can help address diverse learning needs and promote a more inclusive learning environment. However, several challenges must be addressed, including technological disparities, limited infrastructure, and ethical concerns related to AI usage, such as data privacy and algorithmic bias. The study concludes that the successful implementation of AI in inclusive education requires collaborative efforts among governments, educational institutions, and other stakeholders to ensure accessibility, equity, and sustainability. Key recommendations include the development of supportive policies, enhancement of digital literacy among educators and students, and investment in technological infrastructure to bridge the digital divide. This research contributes to the growing discourse on the integration of AI in education, providing insights for policymakers and practitioners aiming to harness AI's potential for inclusive education.
Contextual Framework for Remote Intelligent Monitoring and Detection System for Prediction of Pregnancy Complications Udonna, Uduakobong; Umoren, Imeh; Ansa, Godwin
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.v4i2.244

Abstract

Maternal health disorders can cause complications and harmful incidents in women during pregnancy. To minimize risks, this research developed a platform powered by a supervised machine learning model (SMLM) to support remote intelligent monitoring and prediction of pregnancy complications caused by hypertensive disorder, gestational diabetes, and related indicators. The study used real-world datasets with six UCI Machine Learning Repository features to identify and predict Maternal Health Risk (MHR) factors. A Support Vector Machine (SVM) algorithm was applied to construct the classifier model, which was trained and evaluated using StratifiedKFold cross-validation (k=10). The model achieved 80% accuracy with a precision, recall, and F1-score of 77%. The outcome of this work is the P-Health mobile application, designed to record and track blood pressure, blood sugar, heart rate, body temperature, and weight, while predicting the risk level of pregnancy complications through inference from the integrated machine learning model. Developed with Kotlin and Android Studio, the application enables healthcare practitioners to remotely monitor patients’ vitals in real time. This innovation addresses the challenge of early detection of pregnancy complications and provides continuous monitoring and assessment. The findings suggest that P-Health can improve early detection and timely intervention, helping medical specialists minimize maternal health risks. The system also has the potential to raise public awareness of maternal health issues, thereby contributing to the prevention of complications during pregnancy.
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.
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.
Decentralized AI on The Edge: Implementing Federated Learning for Predictive Maintenance in Industrial IoT Systems Supriadi, Candra; Wahyudi , Wiwid; Priyadi, Agus; Jin, Kim So
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.v4i2.281

Abstract

The integration of Artificial Intelligence (AI) into Industrial Internet of Things (IIoT) systems has enhanced predictive maintenance strategies by enabling early detection of faults in machinery. However, centralized AI models often face challenges related to data privacy, latency, and communication overhead in industrial environments. This study aims to develop a decentralized AI framework utilizing Federated Learning (FL) on edge devices to enhance predictive maintenance in a medium-scale manufacturing plant. The proposed system enables local edge nodes to collaboratively train machine learning models without sharing raw data, thereby preserving data privacy and reducing network load. A prototype was developed using embedded edge devices integrated with vibration and temperature sensors to detect machine anomalies. Federated averaging was used to aggregate local models into a global model. Experimental results show that the federated model achieved 91.4% accuracy in anomaly detection, comparable to centralized approaches, while significantly reducing data transmission volume by 68%. This research demonstrates the feasibility of deploying federated learning on resource-constrained edge devices for predictive maintenance in IIoT environments. The findings suggest that decentralized AI at the edge can offer efficient, privacy-preserving, and scalable solutions for industrial applications
Optimization of Smart Home Energy Consumption Using Machine Learning-Based Load Forecasting Rudiyanto, Arif Rifan; Satria, Bagas Panji ; Panjaitan, Haposan Daniel
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.v4i2.437

Abstract

The growing demand for energy efficiency in smart homes necessitates accurate short-term load forecasting to enable adaptive scheduling and optimal resource allocation. Traditional forecasting models, such as Random Forest, have demonstrated limited capability in capturing sequential dependencies, especially under fluctuating consumption behaviors typical of residential environments. This study aims to compare the forecasting performance of RF and Long Short-Term Memory (LSTM) models in predicting household energy consumption, to identify the most suitable approach for intelligent energy management systems. A quantitative experimental design was adopted using a publicly available dataset, which underwent preprocessing including time normalization and unit conversion. Both models were evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess forecasting accuracy. The LSTM model achieved a lower MAE of 3.2 and RMSE of 4.1, significantly outperforming the RF model, which recorded an MAE of 6.5 and RMSE of 8.4. Additionally, during peak load conditions, LSTM achieved 89.7% accuracy, compared to 72.4% for RF, further emphasizing its superior adaptability to time-sensitive variations. The results confirm that LSTM is more effective in modeling temporal patterns and handling volatility in household energy usage. This research contributes to the field by reinforcing the applicability of deep learning for real-time energy forecasting, offering valuable insights for the development of smart home systems. Future studies may expand this work by integrating hybrid optimization techniques and exploring multi-household scenarios for broader scalability.

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