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International Journal of Technology and Modeling
Published by Etunas Sukses Sistem
ISSN : -     EISSN : 29646847     DOI : https://doi.org/10.63876/ijtm
Core Subject : Science, Education, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics
International Journal of Technology and Modeling (e-ISSN: 2964-6847) is a peer-reviewed journal as a publication media for research results that support research and development of technology and modeling published by Etunas Sukses Sistem. International Journal of Technology and Modeling is published every four months (April, August, December). This journal is expected to be a vehicle for publishing research results from practitioners, academics, authorities, and related communities. IJTM aims to publish high-quality, original research, theoretical studies, and practical applications while promoting a global perspective on technology and modeling. The journal is dedicated to providing a forum for knowledge exchange and fostering cross-disciplinary collaboration, ensuring that research published within its pages contributes to the advancement of science and technology worldwide.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 55 Documents
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The Role of AI-Powered Chatbots in Telemedicine: Improving Accessibility and Patient Engagement Karim, Mahit Kumaris; Sharma, Rajesh; Singh , Vikram
International Journal of Technology and Modeling Vol. 2 No. 1 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i1.108

Abstract

The integration of Artificial Intelligence (AI) in telemedicine has significantly transformed healthcare accessibility and patient engagement. AI-powered chatbots serve as virtual assistants, providing real-time medical guidance, symptom assessment, and appointment scheduling, thereby reducing the burden on healthcare professionals and improving patient experiences. This paper explores the role of AI-driven chatbots in enhancing telemedicine services by analyzing their capabilities in symptom triage, personalized health recommendations, and patient communication. Furthermore, we discuss the advantages and limitations of AI chatbots, focusing on their impact on remote healthcare delivery, data privacy concerns, and user satisfaction. By evaluating recent advancements and real-world applications, this study highlights the potential of AI chatbots to bridge healthcare gaps, particularly in underserved regions. The findings suggest that AI-powered chatbots can enhance healthcare efficiency, improve accessibility, and foster better patient engagement, paving the way for a more inclusive and technology-driven medical ecosystem.
Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction Patel, Priya; Sharma, Arjun; Mehta, Rahul; Iyer, Ananya
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.109

Abstract

Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
Next-Generation Autonomous Vehicles Enhancing Safety and Efficiency with Deep Learning Yılmaz, Mehmet; Demir, Ayşe; Kaya, Emre; Çelik, Zeynep; Özkan, Burak; Şahin, Elif
International Journal of Technology and Modeling Vol. 2 No. 1 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i1.110

Abstract

The rapid advancement of deep learning has significantly transformed the development of next-generation autonomous vehicles, enhancing both safety and efficiency. This paper explores the integration of deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning, in perception, decision-making, and control systems of autonomous vehicles. By leveraging vast datasets and real-time processing, deep learning enables precise object detection, path planning, and adaptive driving strategies. Furthermore, the implementation of sensor fusion techniques combining LiDAR, radar, and cameras enhances situational awareness, reducing the risk of accidents. Despite these advancements, challenges such as computational complexity, adversarial robustness, and ethical considerations remain key research areas. This study provides an overview of the current state-of-the-art deep learning applications in autonomous vehicles and discusses future directions toward fully autonomous, safer, and more efficient transportation systems.
Revolutionizing Natural Language Processing (NLP): Cutting-edge Deep Learning Models for Chatbots and Machine Translation Arif, Muhamad; Saefurohman, Asep; Saluky
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.111

Abstract

Natural Language Processing (NLP) has undergone a transformative evolution with the advent of deep learning, enabling significant advancements in chatbots and machine translation. This article explores state-of-the-art deep learning models, including Transformer-based architectures such as GPT, BERT, and T5, which have revolutionized the way machines understand and generate human language. We analyze how these models enhance chatbot interactions by improving contextual understanding, coherence, and response generation. Additionally, we examine their impact on machine translation, where neural models have surpassed traditional statistical approaches in accuracy and fluency. Despite these advancements, challenges remain, including computational costs, bias mitigation, and real-world deployment constraints. This article provides a comprehensive overview of recent breakthroughs, discusses their implications, and highlights future research directions in NLP-driven AI applications.
Enhancing Predictive Maintenance in Manufacturing Using Deep Learning-Based Anomaly Detection Ardito, Samuel; Setiawan, Wahyu; Wibisono, Agung
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.112

Abstract

Predictive maintenance has become a critical strategy in modern manufacturing to reduce downtime, optimize operational efficiency, and minimize maintenance costs. Traditional approaches, such as rule-based and statistical methods, often fail to detect complex patterns and early signs of system failures. This paper explores the application of deep learning-based anomaly detection techniques to enhance predictive maintenance in manufacturing. Specifically, we investigate the use of autoencoders, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for identifying anomalies in sensor data collected from industrial equipment. Our proposed framework enables early fault detection by learning complex temporal and spatial patterns in machinery behavior. Experimental results demonstrate that deep learning models significantly improve anomaly detection accuracy compared to conventional methods, thereby facilitating timely maintenance interventions and reducing unexpected failures. The findings highlight the potential of deep learning in revolutionizing predictive maintenance, ensuring higher reliability and efficiency in manufacturing systems.
Optimizing Supply Chain Management with Reinforcement Learning: A Data-Driven Approach Purwanto, Adi; Maesaroh, Siti; Sulistyo, Agung
International Journal of Technology and Modeling Vol. 1 No. 3 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i3.113

Abstract

Effective supply chain management (SCM) is crucial for improving efficiency, reducing costs, and enhancing responsiveness in dynamic market conditions. Traditional SCM optimization methods often rely on static models that struggle to adapt to uncertainty and real-time changes. In this study, we propose a data-driven approach using reinforcement learning (RL) to optimize decision-making in SCM. By leveraging historical and real-time data, our RL model dynamically learns optimal inventory policies, demand forecasting strategies, and logistics planning to minimize costs and maximize service levels. We evaluate the performance of our approach through simulations and real-world case studies, demonstrating significant improvements over conventional optimization techniques. The results highlight the potential of RL in transforming SCM by enabling adaptive, intelligent decision-making in complex and uncertain environments.
Integrating IoT and Modelling for Predictive Maintenance in Industry 4.0 Rodriguez, Vincent Emmanuel; Navarro, Camille Therese; Alonzo, Joshua Miguel
International Journal of Technology and Modeling Vol. 1 No. 3 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i3.114

Abstract

This research presents an innovative approach to predictive maintenance by integrating Internet of Things (IoT) technology with advanced analytical modeling within the Industry 4.0 framework. The proposed system harnesses real-time data acquired from IoT sensors and combines it with machine learning algorithms and digital twin simulations to facilitate early detection of potential equipment failures. This hybrid strategy enables proactive maintenance scheduling, significantly reducing unplanned downtime and operational costs. A case study in the manufacturing sector illustrates that the interdisciplinary integration of sensor-based data and intelligent modelling not only enhances operational efficiency but also supports digital transformation by providing a flexible and responsive framework for addressing complex industrial challenges. The primary contribution of this study is the seamless unification of real-time data acquisition and predictive analytics, which lays the groundwork for the next generation of comprehensive predictive maintenance systems in the Industry 4.0 era.
AI-Powered Tools for Personalized Learning in Educational Technology Amoako, Kwame; Asante, Akosua; Owusu, Kwabena
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.115

Abstract

In the digital era, the integration of Artificial Intelligence (AI) in educational technology has opened new avenues for optimizing the learning process through personalized approaches. This article proposes an innovative AI-based framework that combines predictive analytics, dynamic modelling of student learning profiles, and adaptive algorithms to craft learning experiences tailored to individual needs. The research methodology encompasses a systematic literature review, empirical case studies, and controlled experiments to evaluate the effectiveness of AI-powered educational tools. Findings indicate that this personalized approach significantly enhances student engagement, knowledge retention, and academic performance compared to traditional methods. The primary contribution of this study lies in the development of a flexible and scalable personalization model, alongside strategic AI integration practices applicable across diverse educational settings. These insights not only underscore the transformative potential of AI in education but also lay the groundwork for developing technology-driven solutions that address individual learning requirements and mitigate disparities in access to quality education.
Computational Modelling of Fluid Dynamics for Real-world Applications Mai, Cao Thị; Dũng, Bùi Anh; Tùng, Hoàng Thanh
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.117

Abstract

This study presents an innovative computational framework for modelling fluid dynamics in real-world applications. The proposed approach effectively simulates turbulent flows, fluid-structure interactions, and heat transfer processes by integrating advanced numerical methods with optimised algorithms. The model developed through adaptations of the Navier–Stokes equations, was rigorously validated using comprehensive experimental trials. The experimental results demonstrated that the simulations achieved an accuracy within 5% of the observed measurements, confirming the model’s reliability in replicating complex physical phenomena. These findings not only enhance our fundamental understanding of fluid behaviour but also provide valuable insights for design optimisation and system management across various industrial sectors.
The Role of Virtual Reality in Enhancing Skill-Based Training Programs Mehta, Amitabh Rohan; Verma, Sanjana Devi; Yadav, Arjun Pratap
International Journal of Technology and Modeling Vol. 1 No. 3 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i3.118

Abstract

Virtual Reality (VR) has emerged as a transformative technology in skill-based training programs, offering immersive and interactive learning environments that enhance practical competencies across various industries. This paper explores the role of VR in improving training effectiveness, engagement, and knowledge retention. By simulating real-world scenarios, VR enables learners to practice tasks in a safe and controlled setting, reducing the risks and costs associated with traditional hands-on training. The study examines key benefits, such as enhanced experiential learning, personalized feedback, and scalability, while also addressing challenges like hardware limitations, development costs, and user adaptability. Through a review of existing research and case studies, this article highlights the growing adoption of VR in fields such as healthcare, manufacturing, and aviation, demonstrating its potential to revolutionize modern training methodologies. The findings suggest that integrating VR into skill-based training programs can significantly improve learning outcomes, making it a valuable tool for workforce development in the digital age.

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