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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
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Search results for , issue "Vol. 3 No. 1 (2024)" : 5 Documents clear
Medical Image Reconstruction in MRI Using Interpolation Liya, Abel; Ningsih, Resti; Hidayat, Rafi; Firdaus, Taufik Ramadhan
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.v3i1.99

Abstract

Medical image reconstruction is a crucial element in magnetic resonance imaging (MRI) to produce high-quality images that support clinical diagnosis. This study aims to develop a medical image reconstruction method based on interpolation techniques that improves spatial accuracy and visual detail in MRI imaging results. The methodology used includes the implementation of bilinear and bicubic interpolation algorithms to process signal data obtained from MRI imaging. The dataset used in this study is brain MRI data from an open database that has been validated. The results show that the bilinear interpolation method provides higher computing speed, while bicubic interpolation produces better visual details on edges and small structures. Quantitative analysis using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics showed an improvement in the quality of the reconstruction images compared to conventional methods. In the brain dataset trial, bicubic interpolation recorded an average PSNR of 38.7 db and SSIM of 0.94, showing a significant improvement compared to the standard method. This research contributes to reducing artifacts and blurring in MRI reconstruction results, thus supporting more accurate medical decision-making. The implementation of this method also shows great potential to be applied in a variety of other clinical applications, such as soft tissue or internal organ imaging. This research is expected to be integrated with deep learning techniques to improve the efficiency and performance of medical image reconstruction in real time.
Revolutionizing Natural Language Processing (NLP): Cutting-edge Deep Learning Models for Chatbots and Machine Translation Arif, Muhamad; Saefurohman, Asep; Saluky
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.v3i1.111

Abstract

Natural Language Processing (NLP) has undergone a transformative evolution with the advent of deep learning, enabling significant advancements in chatbots and machine translation. This article explores state-of-the-art deep learning models, including Transformer-based architectures such as GPT, BERT, and T5, which have revolutionized the way machines understand and generate human language. We analyze how these models enhance chatbot interactions by improving contextual understanding, coherence, and response generation. Additionally, we examine their impact on machine translation, where neural models have surpassed traditional statistical approaches in accuracy and fluency. Despite these advancements, challenges remain, including computational costs, bias mitigation, and real-world deployment constraints. This article provides a comprehensive overview of recent breakthroughs, discusses their implications, and highlights future research directions in NLP-driven AI applications.
Enhancing Predictive Maintenance in Manufacturing Using Deep Learning-Based Anomaly Detection Ardito, Samuel; Setiawan, Wahyu; Wibisono, Agung
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.v3i1.112

Abstract

Predictive maintenance has become a critical strategy in modern manufacturing to reduce downtime, optimize operational efficiency, and minimize maintenance costs. Traditional approaches, such as rule-based and statistical methods, often fail to detect complex patterns and early signs of system failures. This paper explores the application of deep learning-based anomaly detection techniques to enhance predictive maintenance in manufacturing. Specifically, we investigate the use of autoencoders, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for identifying anomalies in sensor data collected from industrial equipment. Our proposed framework enables early fault detection by learning complex temporal and spatial patterns in machinery behavior. Experimental results demonstrate that deep learning models significantly improve anomaly detection accuracy compared to conventional methods, thereby facilitating timely maintenance interventions and reducing unexpected failures. The findings highlight the potential of deep learning in revolutionizing predictive maintenance, ensuring higher reliability and efficiency in manufacturing systems.
AI-Powered Tools for Personalized Learning in Educational Technology Amoako, Kwame; Asante, Akosua; Owusu, Kwabena
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.v3i1.115

Abstract

In the digital era, the integration of Artificial Intelligence (AI) in educational technology has opened new avenues for optimizing the learning process through personalized approaches. This article proposes an innovative AI-based framework that combines predictive analytics, dynamic modelling of student learning profiles, and adaptive algorithms to craft learning experiences tailored to individual needs. The research methodology encompasses a systematic literature review, empirical case studies, and controlled experiments to evaluate the effectiveness of AI-powered educational tools. Findings indicate that this personalized approach significantly enhances student engagement, knowledge retention, and academic performance compared to traditional methods. The primary contribution of this study lies in the development of a flexible and scalable personalization model, alongside strategic AI integration practices applicable across diverse educational settings. These insights not only underscore the transformative potential of AI in education but also lay the groundwork for developing technology-driven solutions that address individual learning requirements and mitigate disparities in access to quality education.
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.

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