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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. 2 (2023)" : 5 Documents clear
Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction Patel, Priya; Sharma, Arjun; Mehta, Rahul; Iyer, Ananya
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.109

Abstract

Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
Computational Modelling of Fluid Dynamics for Real-world Applications Mai, Cao Thị; Dũng, Bùi Anh; Tùng, Hoàng Thanh
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.117

Abstract

This study presents an innovative computational framework for modelling fluid dynamics in real-world applications. The proposed approach effectively simulates turbulent flows, fluid-structure interactions, and heat transfer processes by integrating advanced numerical methods with optimised algorithms. The model developed through adaptations of the Navier–Stokes equations, was rigorously validated using comprehensive experimental trials. The experimental results demonstrated that the simulations achieved an accuracy within 5% of the observed measurements, confirming the model’s reliability in replicating complex physical phenomena. These findings not only enhance our fundamental understanding of fluid behaviour but also provide valuable insights for design optimisation and system management across various industrial sectors.
Modelling the Impact of Climate Change on Agricultural Productivity: Case Studies from Developing Nations Gupta, Aarav Sharma; Kumar, Rahul; Desai, Meera; Shah, Rohan; Mehta, Neha
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.122

Abstract

Climate change poses a significant threat to agricultural productivity, particularly in developing nations where agriculture remains a primary livelihood source. This study presents a comprehensive modelling approach to assess the impact of climate variability on agricultural output, with a focus on case studies from India. Using a combination of climate projection data, crop simulation models, and econometric analyses, the research evaluates changes in temperature, precipitation patterns, and extreme weather events, and their implications for key staple crops such as rice and wheat. The study highlights regional disparities in vulnerability, adaptive capacity, and yield outcomes across different agro-climatic zones in India. Results indicate that without effective adaptation strategies, agricultural productivity could decline significantly in the coming decades, exacerbating food insecurity and rural poverty. The findings underscore the urgency of integrating climate resilience into national agricultural policies and promoting climate-smart agricultural practices. This research contributes to a broader understanding of how climate change affects agriculture in developing contexts and offers a methodological framework applicable to other regions facing similar challenges.
Efficient Resource Allocation in Cloud Computing Environments: A Modelling Perspective Reddy, Pooja; Verma, Akash; Verma, Kunal; Singh, Abhinav; Soni, Aryan
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.124

Abstract

Efficient resource allocation remains a critical challenge in cloud computing environments due to the dynamic and heterogeneous nature of workloads and infrastructure. This paper presents a comprehensive modelling perspective to address the complexities of resource management, aiming to optimize performance while minimizing operational costs. We propose a flexible and scalable modelling framework that integrates workload characterization, predictive demand analysis, and optimization algorithms to support decision-making in resource allocation. The framework is validated through extensive simulations using real-world workload traces and benchmark scenarios. Results demonstrate significant improvements in resource utilization, energy efficiency, and service-level agreement (SLA) compliance compared to existing approaches. This study highlights the importance of model-driven strategies in enhancing the adaptability and efficiency of cloud resource management systems.
Natural Language Processing for Interactive and Personalized Qur’anic Education Agustina, Dinda; Maryam, Maryam; Marhamah, Siti
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.130

Abstract

The development of artificial intelligence technology, particularly Natural Language Processing (NLP), has opened significant opportunities for transforming Qur’anic learning methods. NLP, as a branch of AI focused on the interaction between computers and human languages, offers new approaches to understanding, analyzing, and teaching the text of the Qur’an in a more interactive and personalized manner. This article examines the utilization of NLP technology in the context of Qur’anic education, from the application of Arabic word morphology analysis to paragraph search systems based on meaning, and the development of virtual assistants capable of answering questions about the contents of the Qur’an. This approach not only enhances accessibility and learning efficiency but also strengthens semantic and contextual understanding of the holy verses. The study also highlights linguistic challenges in processing classical Arabic, as well as the importance of quality annotations and digital corpora. Through a literature review and case study implementation, this article demonstrates that the integration of NLP in Qur’anic learning is a strategic step to enrich Islamic education methods in the digital era, while also bridging the younger generation to the values of the Qur’an through relevant technology.

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