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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 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2024)" : 5 Documents clear
Polynomial Interpolation in Flight Schedule Planning Nurillathifah, Azsky Azkiyyatunnafsi; Hikmah, Nurul
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.v3i2.77

Abstract

Flight schedule planning is a crucial aspect in the air transportation industry to ensure operational efficiency and customer satisfaction. One of the mathematical methods that can be used in such planning is polynomial interpolation. This study aims to analyze the application of the polynomial interpolation method in optimizing flight schedules, especially to predict departure and arrival times based on historical data. Polynomial interpolation is used because of its ability to model non-linear relationships from a series of data points. In this study, the data used included actual flight times on a specific route over a specific period. The Lagrange and Newton interpolation method was applied to build a predictive model of flight schedules. The results show that polynomial interpolation can provide a fairly accurate prediction of flight time, with minimal deviation compared to the actual schedule. Additionally, this method helps in detecting frequent anomalies and delays, allowing for better schedule planning. However, computational complexity increases as the amount of data grows, which becomes a challenge in large-scale deployments. Thus, polynomial interpolation can be an effective tool in planning flight schedules, especially for airlines in improving punctuality and operational efficiency. This research is expected to contribute to the development of a decision support system in flight schedule management.
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.
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.
Advancements in Deep Learning: A Comprehensive Survey on Architectures, Optimization Techniques, and Applications Delgado, Samantha Joyce; Panganiban, Nathaniel Joseph; Robles, Kimberly Anne; Buenaventura, Anthony Daniel; Vergara, Melissa Jane; Evangelista, Christian Noel
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.v3i2.141

Abstract

Deep learning has revolutionized the field of artificial intelligence by enabling significant advancements across various domains, including computer vision, natural language processing, and speech recognition. This survey provides a comprehensive overview of recent developments in deep learning, focusing on three core aspects: architectural innovations, optimization strategies, and real-world applications. We explore the evolution of neural network architectures, from classical feedforward networks to cutting-edge models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs). In addition, we examine state-of-the-art optimization techniques, including adaptive learning rate methods, regularization strategies, and training heuristics that address challenges like vanishing gradients and overfitting. Finally, we present a broad spectrum of deep learning applications, highlighting breakthroughs in autonomous systems, healthcare, finance, and more. By synthesizing recent research trends and identifying emerging challenges, this survey aims to serve as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of deep learning.
Intelligent RPA for Urban Permit Application Workflows Anh, Nguyễn Minh; Bảo, Trần Quốc; Phúc, Lê Hoàng
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.v3i2.145

Abstract

The digital transformation of urban management has paved the way for the integration of intelligent systems aimed at optimizing municipal workflows. One such system is Robotic Process Automation (RPA), which, when enhanced with Artificial Intelligence (AI), offers substantial improvements in automating repetitive tasks. This paper explores the application of Intelligent RPA in urban permit application workflows, specifically focusing on its potential to streamline the processes of permit requests, review, approval, and issuance in urban governance. The paper begins by identifying the current inefficiencies within traditional urban permit systems, such as delays in processing times, human errors, and lack of transparency. By integrating AI-driven decision-making capabilities, Intelligent RPA offers solutions to mitigate these issues, enabling real-time processing, predictive analytics for decision support, and seamless interaction across multiple government departments. Furthermore, this system can adapt to dynamic urban environments, accommodating changes in regulations or requirements. We present a conceptual framework that combines machine learning algorithms and natural language processing (NLP) to automate document verification, permit categorization, and policy compliance checks. The proposed system not only reduces operational costs and processing times but also improves citizen satisfaction by providing faster, more transparent services. The paper concludes with an analysis of potential challenges, including system integration complexities and data privacy concerns, while highlighting future directions for research in intelligent RPA within the context of smart cities.

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