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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. 2 No. 3 (2023)" : 5 Documents clear
Predicting Air Pollution Using Simpson Integration Karmilah; Nazwa
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.82

Abstract

Increasing air pollution, especially in urban areas, is a serious issue that has a negative impact on public health and the environment. Accurate prediction of air pollution levels is critical to supporting mitigation efforts and data-driven decision-making. This study aims to develop an air pollution prediction model using the Simpson Integration method, a numerical approach used to calculate integrals with a high degree of accuracy. The data used included concentrations of pollutants such as PM2.5, PM10, and NO2 taken from daily measurements for one year. This method utilizes an interpolation algorithm to model changes in pollutant concentrations as a function of time. Simpson integration is used to calculate the area under the daily pollutant curve that represents the accumulated exposure to air pollution. The results show that this method is able to provide accurate predictions with an average error rate of less than 5% compared to actual data. This model has advantages in computational efficiency over conventional methods such as simple linear regression analysis. These findings prove that Simpson Integration can be effectively applied in air quality prediction and provide important information for governments and the public. This system is expected to support the development of an air pollution early warning system to increase public awareness and help formulate more responsive environmental policies.
Hybrid Modeling Approaches for Solving Multi-Scale Problems in Engineering Chavez, Janelle Sophia; Mercado, Tristan Alexander
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.132

Abstract

Multi-scale problems in engineering often require modeling approaches that effectively integrate phenomena occurring across different spatial and temporal scales. Hybrid modeling approaches provide a promising solution by combining the strengths of numerical and analytical methods from various scales to enhance both accuracy and computational efficiency. This article reviews a range of hybrid techniques for addressing multi-scale challenges, including the integration of micro- and macro-scale models, coupling discrete and continuum simulations, and the application of multilevel methods in engineering analysis. Case studies from diverse engineering disciplines are presented to illustrate the potential benefits and challenges of hybrid modeling approaches. Leveraging these methods aims to deliver more realistic engineering solutions while optimizing computational resources.
Applying AI Models to Analyze Student Learning Interests Through Digital Interaction Patterns Agyemang, Akosua; Mensah, Kofi; Owusu, Esi
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.142

Abstract

In the digital era, students increasingly engage with learning platforms that generate vast amounts of interaction data. This study explores the application of Artificial Intelligence (AI) models to analyze students' learning interests based on their digital interaction patterns. By leveraging machine learning algorithms and behavioral analytics, we identify correlations between user activities—such as clickstreams, time spent on content, and interaction frequencies—and subject preferences. The study utilizes a dataset from an online learning management system and applies classification and clustering techniques to detect interest trends among students. Results show that AI models can effectively predict individual learning preferences and offer insights to personalize educational content. These findings highlight the potential of integrating AI-driven analytics in education to enhance learner engagement and optimize teaching strategies.
Augmented Modeling Activities to Support Conceptual Thinking in Physics Bakari, Amina Zainab; Mensah, Daniel Kwame; Ngugi, Fatima Leila
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.143

Abstract

This article explores the implementation of augmented modeling activities as a pedagogical approach to enhance conceptual thinking in physics education. By integrating physical modeling with digital augmentation—such as simulations, augmented reality, or interactive visualization tools—students are encouraged to actively construct and revise mental models of physical phenomena. The study investigates how these hybrid modeling environments influence learners’ conceptual understanding, engagement, and problem-solving abilities. Drawing on classroom interventions and qualitative analysis, the findings suggest that augmented modeling not only makes abstract concepts more tangible but also promotes deeper reasoning, hypothesis testing, and collaborative learning. Implications for instructional design and the integration of technology in science education are discussed.
Enhancing Higher-Order Thinking Skills Through Multimedia-Based Inquiry Learning Nugraha, Ketut Budi; Ade, Abdul
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.144

Abstract

The development of higher-order thinking skills (HOTS) is essential in science education to foster students’ critical, analytical, and problem-solving abilities. This study explores the effectiveness of multimedia-based inquiry learning in enhancing HOTS among middle school students in science subjects. By integrating multimedia elements—such as animations, simulations, and interactive modules—into an inquiry-based learning framework, students are encouraged to actively engage with scientific concepts through exploration, questioning, and evidence-based reasoning. A quasi-experimental design was employed, involving two groups: one experiencing traditional instruction and the other receiving multimedia-based inquiry learning. The results showed a significant improvement in HOTS among students in the experimental group, as measured by standardized HOTS assessments and classroom performance tasks. This study highlights the potential of multimedia-enhanced inquiry learning as a powerful pedagogical approach to promote deeper understanding and cognitive engagement in science education.

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