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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. 4 No. 3 (2025)" : 5 Documents clear
A Lightweight Interpolation Framework for Real-Time Travel Time Estimation with Incomplete Traffic Observations Syifani, Alya; Musyarofah, Musyarofah; Firdaus, Taufik Ramadhan
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.88

Abstract

Real-time travel time estimation is essential for intelligent transportation systems (ITS), yet operational traffic data streams are often incomplete due to sensor failures, communication delays, and limited coverage. This paper investigates the effectiveness of interpolation techniques for reconstructing temporally continuous travel-time profiles from real-time speed and density observations. Two approaches—linear interpolation and spline interpolation—are implemented and evaluated across varying traffic regimes (normal flow, dense traffic, and extreme congestion). Model performance is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) against reference travel-time measurements. The results show that interpolation-based methods consistently outperform a conventional baseline relying on average observed speeds, improving estimation accuracy by up to approximately 15%. Linear interpolation yields competitive performance under stable conditions, while spline interpolation achieves lower MAE and RMSE under congestion, indicating stronger robustness to nonlinear traffic dynamics. Additionally, interpolation improves service availability and estimated time of arrival (ETA) reliability with minimal computational overhead, supporting practical deployment in resource-constrained environments. These findings suggest that interpolation provides a lightweight and effective enhancement for real-time travel time estimation and can serve as a reliable preprocessing layer for advanced predictive models in future work.
Interpretable Short-Term Weather Prediction via Singular Value Decomposition and Linear System Modeling Agustine, Amelia Nur; Maisafatin, Selma Kayla; Hidayat, Rafi
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.93

Abstract

Short-term weather prediction plays a critical role in supporting decision-making across sectors such as agriculture, transportation, and disaster risk management. This study proposes an interpretable and computationally efficient weather forecasting approach based on linear system modeling combined with Singular Value Decomposition (SVD). Historical meteorological data—including temperature, humidity, air pressure, and wind speed—are represented in matrix form to extract dominant patterns and construct a system of linear equations describing inter-variable relationships. The resulting model is evaluated for short-term forecasting horizons of 24–48 hours using standard performance metrics. Experimental results demonstrate that the proposed SVD-based linear system model outperforms conventional linear regression, achieving lower MAE and RMSE values and higher coefficients of determination (R² = 0.94 for temperature and 0.91 for humidity). While not intended to replace physics-based numerical weather prediction models for long-term forecasting, the proposed approach offers significant advantages in computational speed, interpretability, and applicability in data- and resource-constrained environments. These findings indicate that matrix-based linear system analysis provides a viable alternative for fast and accurate short-term weather prediction and can be further enhanced through integration with non-linear or machine learning-based methods.
Unmanned Aerial Vehicles (UAVs) for Pest and Disease Detection in Rice Cultivation: A Systematic Review Saluky, Saluky; Marine, Yoni; Fatimah, Aisya
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.158

Abstract

This paper presents a systematic review of the use of Unmanned Aerial Vehicles (UAVs) for pest and disease detection in rice cultivation, a critical challenge in maintaining yield stability and reducing chemical overuse in global food systems. The study aims to synthesize current approaches, technologies, and algorithms employed in UAV-based monitoring of rice pests and diseases, while identifying research gaps and future directions for precision rice farming. Following PRISMA-inspired guidelines, a Systematic Literature Review (SLR) was conducted across major scientific databases (Scopus, Web of Science, IEEE Xplore, and ScienceDirect) using predefined keyword combinations related to UAVs, rice, pest/disease detection, and remote sensing. Inclusion criteria focused on peer-reviewed studies that explicitly employed aerial platforms for detecting biotic stress in rice, while review papers, non-rice crops, and purely simulation-based works were excluded. The findings highlight three dominant technology dimensions: sensing modalities, with RGB and multispectral imagery being most prevalent, followed by hyperspectral and thermal sensors; analytical methods, ranging from traditional vegetation indices and thresholding to advanced machine learning and deep learning models; and operational considerations, including flight altitude, spatial resolution, and temporal frequency of data acquisition. The review contributes by proposing a conceptual framework linking sensor choice, image processing pipelines, and pest/disease symptom characteristics in rice, and by outlining open challenges regarding data standardization, smallholder adoption, and model transferability across regions.
Climate Change Mitigation: Applications of Advanced Modeling Techniques Gan, Keemo; Huang, Chung
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.159

Abstract

Climate change poses one of the most pressing challenges to global sustainability, necessitating comprehensive mitigation strategies informed by robust scientific analysis. This article examines the role of advanced modeling techniques in enhancing climate change mitigation efforts across multiple scales and sectors. We explore recent developments in integrated assessment models, machine learning algorithms, and high-resolution climate simulations that enable more accurate projections of future climate scenarios and their socioeconomic impacts. The study discusses how these sophisticated computational approaches facilitate the evaluation of mitigation pathways, including renewable energy transitions, carbon capture technologies, and nature-based solutions. Particular attention is given to the integration of uncertainty quantification methods and the coupling of physical climate models with economic and land-use models to support evidence-based policy decisions. Case studies demonstrate the application of ensemble modeling techniques, deep learning frameworks, and scenario analysis in identifying cost-effective mitigation strategies at regional and global levels. Results indicate that advanced modeling approaches significantly improve the accuracy of emission reduction projections and enhance our understanding of feedback mechanisms within the climate system. The article also addresses current limitations in data availability, computational constraints, and the challenges of downscaling global projections to local contexts. We conclude that continued refinement of modeling techniques, combined with improved interdisciplinary collaboration and stakeholder engagement, is essential for designing effective climate mitigation policies that can achieve the goals outlined in international climate agreements.
Modelling Smart Cities: Integration of IoT, Big Data, and Analytics Anne Robles, Kimberly; Delgado, Samantha Joyce; Panganiban, Nathaniel Joseph
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.160

Abstract

The rapid urbanization and technological advancement have catalyzed the emergence of smart cities as a transformative paradigm for sustainable urban development. This paper presents a comprehensive framework for modeling smart cities through the systematic integration of Internet of Things (IoT), big data, and analytics technologies. We propose a multi-layered architectural model that addresses the technical, operational, and governance challenges inherent in smart city implementations. The research examines how IoT sensors and devices generate massive volumes of heterogeneous data, which are subsequently processed through big data platforms to extract actionable insights via advanced analytics techniques. Our framework encompasses data acquisition, storage, processing, and visualization layers, while incorporating machine learning algorithms and real-time analytics for intelligent decision-making. Through case studies of various smart city domains including transportation, energy management, public safety, and healthcare, we demonstrate the practical applicability of our integrated model. The paper also addresses critical challenges such as data privacy, security, interoperability, and scalability that must be overcome for successful smart city deployment. Our findings reveal that effective integration of these three technological pillars enables cities to optimize resource allocation, enhance service delivery, improve quality of life for citizens, and achieve sustainability goals. The proposed model provides urban planners, policymakers, and technology implementers with a structured approach to design and deploy smart city solutions that are both technologically robust and contextually relevant.

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