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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. 1 No. 3 (2022)" : 5 Documents clear
Gauss's Elimination to Solve Financial Modeling Models in Banks Dwi Oktavianty, Firda; Inayatussulaimah; Hardianti, Siti
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.v1i3.105

Abstract

Gauss's elimination is an effective mathematical method for solving linear equation systems and is widely applied in various fields, including financial modeling. This article aims to apply Gauss's elimination method in solving complex financial modeling models in banks, especially in credit portfolio analysis and risk management. This study uses a quantitative approach by applying Gauss's elimination to bank financial data, involving a linear equation system that represents the relationship between risk factors, credit interest, and payment capacity. The results of the analysis show that this method is able to provide an efficient and accurate solution in determining the optimal combination of credit portfolios and minimizing default risk. The simulation also confirmed the reliability of Gauss's elimination in handling large-scale data with a variety of financial parameters. The conclusion of the study is that Gauss's elimination is not only relevant in a theoretical context but also highly applicable in the banking industry to improve data-driven decision-making. The contribution of this research to science is to provide an innovative approach to utilize classical mathematical methods in solving modern problems in the financial sector, as well as to provide a basis for further research in the field of linear equation-based financial modeling.
Population Dynamics Modeling with Differential Equation Method Zahra Rustiani Muplihah; Dede Nurohmah; Marine, Yoni; Hidayat, Rafi
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.v1i3.107

Abstract

Population dynamics modeling is one of the important approaches in understanding population development and its influence on various aspects of life, such as economic, social, and environmental. This article discusses the application of differential equation methods in modeling population dynamics, with a focus on the analysis of growth and interactions between populations. The models used include exponential growth models, logistics, and the Lotka-Volterra model to describe competitive interactions and predations between populations. Through numerical simulations and qualitative analysis, this article shows how parameters such as birth rate, mortality, and environmental carrying capacity affect population growth patterns. In addition, the influence of external factors such as government policies and natural disasters is also incorporated into the model to expand the application in real contexts. The results of the analysis show that the differential equation model is able to provide an accurate picture of population dynamics if the parameters are estimated correctly. This article also highlights the importance of model validation using empirical data to ensure prediction reliability. This modeling can be used as a tool in development planning, resource allocation, and risk mitigation in various sectors. The conclusion of this study is that the differential equation method is not only effective in explaining population phenomena, but also flexible to adapt to various dynamic conditions. As such, this approach offers a significant contribution to demographic studies and data-driven decision-making.
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.
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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