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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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Optimizing Urban Transportation Systems Using Simulation and Modelling Soriano, Nicole Beatrice; Villanueva, Adrian Benedict; Santiago, Erika Mae
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.v2i1.120

Abstract

The rapid growth of urban populations has intensified the pressure on transportation infrastructure, leading to challenges such as traffic congestion, increased travel time, pollution, and reduced overall mobility. To address these issues, the use of simulation and modelling has emerged as a powerful approach in understanding and optimizing urban transportation systems. This study investigates how various simulation techniques—such as discrete-event simulation, agent-based modelling, and system dynamics—can be applied to analyze traffic patterns, test policy interventions, and predict system behavior under different scenarios. By integrating real-time data and historical trends, simulation models provide a virtual environment for assessing the impact of traffic management strategies, including signal optimization, public transit prioritization, road pricing, and multi-modal integration. The research presents case studies and comparative analyses that highlight the effectiveness of simulation tools in enhancing decision-making processes for urban planners and policymakers. The findings suggest that strategic use of modelling can reduce congestion, improve efficiency, and support sustainable urban mobility. Furthermore, the study emphasizes the importance of interdisciplinary collaboration and the integration of smart technologies to build more resilient and adaptive transport systems. In conclusion, simulation and modelling play a pivotal role in shaping the future of urban transportation in an increasingly complex and data-driven world.
Predictive Maintenance Strategies for Industry 4.0: A Modelling Approach Subagja, Asep; Watanto, Gunawan; Mujadi, Agus
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.v3i3.121

Abstract

The advent of Industry 4.0 has revolutionized industrial operations by integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics into manufacturing systems. Among its many applications, predictive maintenance emerges as a critical strategy to minimize downtime, reduce operational costs, and enhance asset longevity. This article presents a modelling approach to predictive maintenance tailored for Industry 4.0 environments. We explore how real-time data acquisition and machine learning algorithms can be integrated into a predictive maintenance framework, enabling early fault detection and optimal scheduling of maintenance activities. The study proposes a comprehensive model that incorporates sensor data analysis, failure prediction, and decision support systems. Simulations and case studies demonstrate the effectiveness of the proposed approach in increasing system reliability and efficiency. Our findings highlight the pivotal role of data-driven models in transforming traditional maintenance practices into proactive, intelligent maintenance strategies suitable for smart factories.
Modelling the Impact of Climate Change on Agricultural Productivity: Case Studies from Developing Nations Gupta, Aarav Sharma; Kumar, Rahul; Desai, Meera; Shah, Rohan; Mehta, Neha
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.v2i2.122

Abstract

Climate change poses a significant threat to agricultural productivity, particularly in developing nations where agriculture remains a primary livelihood source. This study presents a comprehensive modelling approach to assess the impact of climate variability on agricultural output, with a focus on case studies from India. Using a combination of climate projection data, crop simulation models, and econometric analyses, the research evaluates changes in temperature, precipitation patterns, and extreme weather events, and their implications for key staple crops such as rice and wheat. The study highlights regional disparities in vulnerability, adaptive capacity, and yield outcomes across different agro-climatic zones in India. Results indicate that without effective adaptation strategies, agricultural productivity could decline significantly in the coming decades, exacerbating food insecurity and rural poverty. The findings underscore the urgency of integrating climate resilience into national agricultural policies and promoting climate-smart agricultural practices. This research contributes to a broader understanding of how climate change affects agriculture in developing contexts and offers a methodological framework applicable to other regions facing similar challenges.
Modelling the Dynamics of Financial Markets: Insights from Agent-Based Models Pascual, Francis Xavier; Tan, Katrina Louise; Ramos, Benedict Angelo
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.v3i1.123

Abstract

The dynamics of financial markets are shaped by complex interactions among heterogeneous agents, often deviating from the assumptions of classical economic theory. This study explores the use of agent-based models (ABMs) as a computational approach to capture the emergent behaviors and nonlinearities inherent in financial systems. By simulating markets with agents possessing bounded rationality, adaptive expectations, and diverse trading strategies, ABMs offer insights into phenomena such as market bubbles, crashes, and volatility clustering. This paper presents a comprehensive framework for modeling financial markets using ABMs, incorporating key elements such as market microstructure, information diffusion, and behavioral rules. Through a series of simulation experiments, we demonstrate how varying agent behaviors influence price dynamics and systemic risk. The findings highlight the capacity of ABMs to replicate empirical stylized facts observed in real-world markets and to serve as a valuable tool for stress-testing regulatory policies. This research contributes to the growing body of literature advocating for computational economics as a complementary lens to understand the evolving landscape of global financial systems.
Efficient Resource Allocation in Cloud Computing Environments: A Modelling Perspective Reddy, Pooja; Verma, Akash; Verma, Kunal; Singh, Abhinav; Soni, Aryan
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.v2i2.124

Abstract

Efficient resource allocation remains a critical challenge in cloud computing environments due to the dynamic and heterogeneous nature of workloads and infrastructure. This paper presents a comprehensive modelling perspective to address the complexities of resource management, aiming to optimize performance while minimizing operational costs. We propose a flexible and scalable modelling framework that integrates workload characterization, predictive demand analysis, and optimization algorithms to support decision-making in resource allocation. The framework is validated through extensive simulations using real-world workload traces and benchmark scenarios. Results demonstrate significant improvements in resource utilization, energy efficiency, and service-level agreement (SLA) compliance compared to existing approaches. This study highlights the importance of model-driven strategies in enhancing the adaptability and efficiency of cloud resource management systems.
A Novel Multi-Scale Agent-Based Modeling Framework for Simulating Complex Adaptive Systems in Urban Environments Morales, Felicity Anne; Mundo, Nathania Gabriel Del; Lim, Joseph Angelo
International Journal of Technology and Modeling Vol. 4 No. 1 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i1.125

Abstract

Urban environments are increasingly recognized as complex adaptive systems, where dynamic interactions between heterogeneous agents—such as individuals, organizations, infrastructure, and environmental components—give rise to emergent behaviors that are difficult to predict using conventional modeling techniques. This paper introduces a novel multi-scale agent-based modeling (MS-ABM) framework designed to capture and simulate these interactions across multiple spatial and temporal resolutions. The proposed framework integrates micro-level behavioral rules with macro-level system constraints, enabling the simultaneous analysis of individual agent decisions and large-scale urban phenomena such as traffic flow, land-use evolution, and resource distribution. A hierarchical communication mechanism is developed to enable bidirectional information exchange between scales, improving model fidelity and responsiveness. The framework is validated using a case study of urban mobility in a rapidly growing metropolitan region, demonstrating its ability to reproduce real-world patterns, adapt to dynamic policy interventions, and support scenario-based decision making. The results highlight the potential of MS-ABM as a robust tool for urban planners, policy makers, and researchers to explore the interplay of local behaviors and global outcomes in complex urban systems.
Sparse System Dynamics Modeling for High-Dimensional Decision-Making in Industrial Automation Kara, Hasan; Karahan, Yasemin; Uysal, Cengiz
International Journal of Technology and Modeling Vol. 4 No. 1 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i1.126

Abstract

The increasing complexity of industrial automation systems has introduced significant challenges in modeling and analyzing high-dimensional decision-making environments. Traditional system dynamics (SD) models often struggle with scalability and computational efficiency when faced with numerous interdependent variables and feedback loops. In this study, we propose a Sparse System Dynamics Modeling (SSDM) approach that leverages sparsity-aware techniques to identify and retain only the most influential causal relationships within complex industrial systems. The SSDM framework introduces a structure reduction mechanism based on variable correlation thresholds and influence-weight pruning, enabling the construction of lightweight yet expressive models. By applying this method to a case study involving automated production line optimization, we demonstrate that SSDM maintains the predictive integrity of full-scale SD models while reducing computational overhead by up to 60%. The model also facilitates faster scenario simulations and more interpretable decision pathways, making it suitable for real-time industrial planning and control. Our results highlight the potential of sparse modeling in addressing the curse of dimensionality in industrial environments, providing a scalable and interpretable alternative for decision-makers in smart manufacturing and Industry 4.0 applications.
Introducing a Hybrid Physics-Informed Neural Network and Finite Element Model for Predicting Structural Deformation Under Dynamic Load Hermanto, Hermanto; Masduki, Ahmad Zaenal; Febriyanto, David
International Journal of Technology and Modeling Vol. 4 No. 1 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i1.127

Abstract

This study introduces a novel hybrid framework that integrates Physics-Informed Neural Networks (PINNs) with the Finite Element Method (FEM) to accurately predict structural deformation under dynamic loading conditions. While FEM remains a powerful tool in structural mechanics, its computational cost rises significantly with complex geometries and time-dependent simulations. To address this, the proposed hybrid model leverages the domain knowledge embedded in partial differential equations through PINNs, which are trained on both synthetic FEM data and governing physics laws. The model enables faster and more generalizable predictions of displacement fields by learning from limited simulation data while enforcing physical consistency. Numerical experiments on beam and plate structures subjected to varying dynamic loads demonstrate that the hybrid approach achieves high accuracy with substantially reduced computational effort compared to traditional FEM-only simulations. This work highlights the potential of combining data-driven and physics-based modeling to support real-time structural health monitoring and decision-making in engineering systems.
Towards Efficient Crowd Counting and Behavior Analysis Using YOLOv11 Lubis, Amanda Amalia; Prasasta, Adrian; Sari, Dewi Anita
International Journal of Technology and Modeling Vol. 4 No. 1 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i1.128

Abstract

The rapid growth of urban populations has intensified the need for robust crowd monitoring systems to ensure public safety and efficient resource management. This study explores the integration of YOLOv11, an advanced real-time object detection model, for crowd counting and behavior analysis in dynamic environments. We propose a hybrid framework that leverages YOLOv11’s high-speed detection capabilities to identify individuals in densely packed scenes and extract behavioral cues such as motion patterns and group interactions. The model is fine-tuned on benchmark datasets to optimize accuracy in varying lighting and occlusion conditions. Experimental results demonstrate that our approach achieves a significant improvement in both counting precision and behavioral feature extraction compared to previous YOLO versions and other baseline models. This research highlights YOLOv11’s potential as a lightweight yet powerful solution for real-time crowd analytics, with applications ranging from smart surveillance to public event management.
Hybrid Deep Learning and Agent-Based Modeling for Dynamic Urban Traffic Forecasting in Smart Cities Gonzales, Charlene Mae; Salazar, Dominic Rafael; Uy, Stephanie Nicole; Lim, Raymond Christopher
International Journal of Technology and Modeling Vol. 4 No. 1 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i1.129

Abstract

Urban traffic systems are becoming increasingly complex due to rapid urbanization and the dynamic nature of mobility patterns in smart cities. Accurate and adaptive forecasting of urban traffic is essential for effective traffic management and sustainable urban planning. This study proposes a hybrid modeling approach that integrates Deep Learning (DL) with Agent-Based Modeling (ABM) to enhance the accuracy and interpretability of traffic forecasting. The deep learning component leverages spatiotemporal data from IoT sensors and historical traffic records to capture nonlinear traffic dynamics, while the agent-based model simulates the behaviors and interactions of individual traffic participants under various scenarios. By combining data-driven prediction with rule-based simulation, the hybrid model can forecast traffic flows and adapt to changes in infrastructure, policy, or user behavior. Experimental evaluations using real-world traffic datasets from a major metropolitan area demonstrate that the proposed model outperforms traditional forecasting techniques in both short-term accuracy and scenario-based flexibility. This research contributes to the development of intelligent transportation systems and offers practical insights for city planners and traffic authorities.

Page 4 of 6 | Total Record : 55

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