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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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Natural Language Processing for Interactive and Personalized Qur’anic Education Agustina, Dinda; Maryam, Maryam; Marhamah, Siti
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.v2i2.130

Abstract

The development of artificial intelligence technology, particularly Natural Language Processing (NLP), has opened significant opportunities for transforming Qur’anic learning methods. NLP, as a branch of AI focused on the interaction between computers and human languages, offers new approaches to understanding, analyzing, and teaching the text of the Qur’an in a more interactive and personalized manner. This article examines the utilization of NLP technology in the context of Qur’anic education, from the application of Arabic word morphology analysis to paragraph search systems based on meaning, and the development of virtual assistants capable of answering questions about the contents of the Qur’an. This approach not only enhances accessibility and learning efficiency but also strengthens semantic and contextual understanding of the holy verses. The study also highlights linguistic challenges in processing classical Arabic, as well as the importance of quality annotations and digital corpora. Through a literature review and case study implementation, this article demonstrates that the integration of NLP in Qur’anic learning is a strategic step to enrich Islamic education methods in the digital era, while also bridging the younger generation to the values of the Qur’an through relevant technology.
Hybrid Modeling Approaches for Solving Multi-Scale Problems in Engineering Chavez, Janelle Sophia; Mercado, Tristan Alexander
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.132

Abstract

Multi-scale problems in engineering often require modeling approaches that effectively integrate phenomena occurring across different spatial and temporal scales. Hybrid modeling approaches provide a promising solution by combining the strengths of numerical and analytical methods from various scales to enhance both accuracy and computational efficiency. This article reviews a range of hybrid techniques for addressing multi-scale challenges, including the integration of micro- and macro-scale models, coupling discrete and continuum simulations, and the application of multilevel methods in engineering analysis. Case studies from diverse engineering disciplines are presented to illustrate the potential benefits and challenges of hybrid modeling approaches. Leveraging these methods aims to deliver more realistic engineering solutions while optimizing computational resources.
The Role of Virtual Reality in Enhancing Skill-Based Training Programs Lan, Tạ Thị; Nhựt, Võ Minh; Long, Hà Bảo
International Journal of Technology and Modeling Vol. 3 No. 2 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i2.133

Abstract

Virtual Reality (VR) technology has emerged as a promising tool to revolutionize skill-based training programs by providing immersive and interactive learning environments. This study explores the impact of VR on enhancing training effectiveness within Vietnam’s workforce development initiatives. Through a mixed-methods approach combining quantitative performance data and qualitative feedback from 300 trainees across manufacturing, healthcare, and vocational education sectors, the research evaluates VR’s role in improving skill acquisition, learner engagement, and retention compared to traditional training methods. The findings indicate that VR-based training significantly increases post-training proficiency scores by an average of 25%, while reducing the time required to achieve competency by approximately 30%. In manufacturing, VR simulations enable trainees to safely practice assembly line operations and error management, resulting in fewer workplace mistakes. In healthcare, VR modules focused on surgical and procedural training improve procedural confidence and adherence to clinical protocols. User surveys reveal high satisfaction levels, particularly appreciating the realistic, risk-free practice environment. However, challenges such as high initial investment costs, limited localized VR content, and technical infrastructure gaps—especially in rural areas—limit widespread adoption. Addressing these barriers requires targeted strategies including content localization, government support, and infrastructure development. This study underscores VR’s potential to enhance skill-based training effectiveness and accelerate workforce readiness in emerging economies like Vietnam. It provides critical insights for policymakers, educators, and industry leaders aiming to integrate advanced technologies into training programs to meet evolving labor market demands.
A Survey on Object Detection in Dynamic and Complex Environments Soni, Ritu; Kumar, Ravi; Jain, Sheetal
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.134

Abstract

Object detection has become a cornerstone of computer vision, with applications ranging from autonomous driving and robotics to surveillance and augmented reality. While substantial progress has been made in controlled and static settings, real-world environments often pose significant challenges due to dynamic backgrounds, occlusions, illumination variations, and cluttered scenes. This survey provides a comprehensive review of recent advancements in object detection specifically tailored for dynamic and complex environments. We classify existing approaches based on their core methodologies, including traditional feature-based techniques, deep learning models, and hybrid frameworks. Key challenges such as real-time performance, adaptability to environmental changes, and robustness to motion are discussed in depth. Furthermore, we analyze benchmark datasets and evaluation metrics commonly used in this domain, highlighting their limitations and suggesting improvements. Finally, we explore emerging trends and future directions, including the integration of spatiotemporal modeling, sensor fusion, and domain adaptation strategies. This survey aims to serve as a valuable reference for researchers and practitioners seeking to develop or apply object detection systems in real-world, unpredictable environments.
Simulating the Effects of Policy Interventions on Socio-Economic Development: Case Studies and Methodologies Verma, Shubhi; Tiwari, Ankit
International Journal of Technology and Modeling Vol. 3 No. 2 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i2.135

Abstract

This study explores the impact of policy interventions on socio-economic development through simulation-based methodologies, focusing on selected case studies in India. Utilizing systems dynamics modeling and agent-based simulations, the research examines how targeted policies in sectors such as education, healthcare, and rural infrastructure influence economic growth, income distribution, and social mobility over time. The case studies include region-specific implementations, allowing for a nuanced understanding of policy effectiveness in diverse socio-economic contexts. Results highlight the interplay between policy design, regional characteristics, and long-term developmental outcomes. The findings offer actionable insights for policymakers aiming to optimize intervention strategies, reduce inequalities, and foster inclusive growth. Methodologically, this paper contributes a comparative framework for simulating policy scenarios, which can be adapted and applied to other developing country contexts.
Revolutionizing Industries: The Role of Technological Innovations in Modern Business Practices Nkrumah, Kwame; Agyemang, Akosua
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.136

Abstract

Technological innovation has emerged as a critical driver in transforming modern business practices across the globe. This study investigates the extent to which technological advancements have reshaped industrial operations and business strategies within the Cameroonian context. Using a mixed-methods approach, we collected data from 150 businesses across various sectors, complemented by in-depth interviews with industry leaders and technology stakeholders. The findings reveal a strong correlation between the adoption of emerging technologies—such as artificial intelligence, cloud computing, and automation—and improvements in productivity, operational efficiency, and market competitiveness. However, the study also highlights persistent challenges, including infrastructure deficits, limited digital literacy, and regulatory constraints that hinder full-scale adoption. Our analysis underscores the need for targeted policy reforms, capacity-building initiatives, and strategic investments to foster a more innovation-friendly ecosystem. This research contributes to the growing body of knowledge on digital transformation in emerging economies and offers actionable insights for business leaders, policymakers, and development practitioners aiming to harness technology for sustainable industrial growth.
A Survey on Deep Learning for Natural Language Processing: Models, Techniques, and Open Research Problems Hào, Nguyễn Nhật; Vy, Trần Khánh; Phước, Lê Văn
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.137

Abstract

In recent years, deep learning has emerged as a powerful paradigm in natural language processing (NLP), enabling significant breakthroughs in tasks such as machine translation, sentiment analysis, and question answering. This survey provides a comprehensive overview of deep learning models and techniques that have shaped the evolution of NLP, with a focused lens on the Vietnamese language as a representative low-resource language. We review foundational models including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformer-based architectures such as BERT and GPT, and analyze their applications in Vietnamese NLP tasks. Special attention is given to the development and adaptation of Vietnamese-specific pretrained language models like PhoBERT and ViT5, as well as the use of multilingual approaches to address data scarcity. In addition, the paper discusses practical implementations in Vietnam, such as sentiment analysis of social media, Vietnamese question answering systems, and machine translation, highlighting the opportunities and challenges in this context. We also identify open research problems including limited training data, dialectal variations, code-switching, and ethical concerns, offering insights and directions for future work. This survey aims to serve as a resource for researchers and practitioners seeking to advance NLP capabilities in low-resource languages using deep learning.
Optimizing Supply Chains Through Technology and Computational Modeling Capulong, Alyssa Jean; Shah, Nisha
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.138

Abstract

In the era of rapid globalization, optimizing supply chains has become essential for enhancing operational efficiency and competitiveness. This study investigates the role of technology and computational modeling in improving supply chain performance in the context of Myanmar, a developing economy with unique logistical and infrastructural challenges. By integrating advanced technologies such as IoT, data analytics, and simulation-based modeling, the research evaluates their impact on demand forecasting, inventory management, and transportation planning. A case study approach involving key sectors such as agriculture and manufacturing was employed to assess real-world applicability. Results indicate significant improvements in supply chain responsiveness, cost reduction, and decision-making accuracy. This paper contributes to the growing body of knowledge by providing insights into how emerging technologies can be effectively applied in developing countries to overcome supply chain inefficiencies. The findings also highlight the importance of tailored technological adoption strategies that consider local socio-economic and infrastructural conditions.
Gamification in E-Learning: Transforming Education through Technology Domingo, Marianne Faith; Bautista, Carlo Andrew; Fajardo, Alyssa Jean
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.139

Abstract

The integration of gamification into e-learning has become a transformative force in education, particularly in developing countries such as the Philippines. This study explores the impact of gamified e-learning platforms on student engagement and academic performance in higher education institutions in the Philippines. Using a mixed-methods approach, data were collected from 150 undergraduate students across three universities through surveys, interviews, and academic performance records. The results show that 82% of participants reported increased motivation and engagement when using gamified learning platforms, and 67% demonstrated improved academic performance compared to those using traditional e-learning methods. Students particularly responded positively to elements such as badges, leaderboards, and point-based systems, which enhanced their sense of competition and achievement. Despite some challenges in implementation—such as internet accessibility and the need for culturally relevant game design—the study concludes that gamification holds significant potential to improve the effectiveness of e-learning in the Philippine educational context.
Exploring Emerging Trends in AI-Driven Technological Advancements Patil, Arya; Yadav, Shweta; Pandey, Manish
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.140

Abstract

The rapid evolution of Artificial Intelligence (AI) has catalyzed a transformative wave across diverse technological landscapes. This study explores emerging trends in AI-driven technological advancements with a focus on developments within the Indian context. Through a mixed-methods approach combining literature analysis, expert interviews, and case studies from India's tech ecosystem, this research identifies key innovation patterns, adoption drivers, and sector-specific applications of AI technologies. Findings reveal significant momentum in areas such as healthcare diagnostics, smart agriculture, fintech automation, and personalized education, fueled by governmental initiatives, startup growth, and increased academic-industry collaboration. Additionally, the study highlights the challenges of ethical governance, data privacy, and digital divide that accompany rapid AI integration. By mapping the trajectory of AI's evolution in India, this research contributes to a deeper understanding of global AI dynamics and offers strategic insights for policymakers, researchers, and technology developers worldwide.

Page 5 of 6 | Total Record : 55

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