cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Saluky
Contact Email
etunaspublisher@gmail.com
Phone
+6289604331800
Journal Mail Official
etunaspublisher@gmail.com
Editorial Address
Jl. Pilang Gg. Sukajaya no 11 Rt/RW 02/10
Location
Kab. cirebon,
Jawa barat
INDONESIA
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. 4 No. 1 (2025)" : 5 Documents clear
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 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025) Vol. 4 No. 1 (2025) Vol. 3 No. 3 (2024) Vol. 3 No. 2 (2024) Vol. 3 No. 1 (2024) Vol. 2 No. 3 (2023) Vol. 2 No. 2 (2023) Vol. 2 No. 1 (2023) Vol. 1 No. 3 (2022) Vol. 1 No. 2 (2022) Vol. 1 No. 1 (2022) More Issue