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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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Advancements in Deep Learning: A Comprehensive Survey on Architectures, Optimization Techniques, and Applications Delgado, Samantha Joyce; Panganiban, Nathaniel Joseph; Robles, Kimberly Anne; Buenaventura, Anthony Daniel; Vergara, Melissa Jane; Evangelista, Christian Noel
International Journal of Technology and Modeling Vol. 3 No. 2 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i2.141

Abstract

Deep learning has revolutionized the field of artificial intelligence by enabling significant advancements across various domains, including computer vision, natural language processing, and speech recognition. This survey provides a comprehensive overview of recent developments in deep learning, focusing on three core aspects: architectural innovations, optimization strategies, and real-world applications. We explore the evolution of neural network architectures, from classical feedforward networks to cutting-edge models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs). In addition, we examine state-of-the-art optimization techniques, including adaptive learning rate methods, regularization strategies, and training heuristics that address challenges like vanishing gradients and overfitting. Finally, we present a broad spectrum of deep learning applications, highlighting breakthroughs in autonomous systems, healthcare, finance, and more. By synthesizing recent research trends and identifying emerging challenges, this survey aims to serve as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of deep learning.
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.
Intelligent RPA for Urban Permit Application Workflows Anh, Nguyễn Minh; Bảo, Trần Quốc; Phúc, Lê Hoàng
International Journal of Technology and Modeling Vol. 3 No. 2 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i2.145

Abstract

The digital transformation of urban management has paved the way for the integration of intelligent systems aimed at optimizing municipal workflows. One such system is Robotic Process Automation (RPA), which, when enhanced with Artificial Intelligence (AI), offers substantial improvements in automating repetitive tasks. This paper explores the application of Intelligent RPA in urban permit application workflows, specifically focusing on its potential to streamline the processes of permit requests, review, approval, and issuance in urban governance. The paper begins by identifying the current inefficiencies within traditional urban permit systems, such as delays in processing times, human errors, and lack of transparency. By integrating AI-driven decision-making capabilities, Intelligent RPA offers solutions to mitigate these issues, enabling real-time processing, predictive analytics for decision support, and seamless interaction across multiple government departments. Furthermore, this system can adapt to dynamic urban environments, accommodating changes in regulations or requirements. We present a conceptual framework that combines machine learning algorithms and natural language processing (NLP) to automate document verification, permit categorization, and policy compliance checks. The proposed system not only reduces operational costs and processing times but also improves citizen satisfaction by providing faster, more transparent services. The paper concludes with an analysis of potential challenges, including system integration complexities and data privacy concerns, while highlighting future directions for research in intelligent RPA within the context of smart cities.

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