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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. 3 No. 3 (2024)" : 5 Documents clear
Determining the Optimal Chemical Concentration with the Regula Falsi Method Bandiyah, Salza Nur; Angelia; Hidayat, Rafi
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.100

Abstract

Determination of optimal chemical concentrations is one of the important aspects in industrial research and applications, especially in chemical reaction processes. In this article, the use of the Regula Falsi method as a numerical approach to determine optimal concentration based on the mathematical model of non-linear functions is discussed. The Regula Falsi method was chosen for its simplicity and ability to iteratively converge solutions with high accuracy. The target function is defined from the relationship between concentration variables and the efficiency of chemical reactions. In this study, simulations were carried out using several reaction parameter data scenarios to evaluate the performance of the method. The results show that the Regula Falsi method consistently provides accurate results in determining the root of the target function that represents the optimal concentration. The error rate is calculated to ensure that the resulting solution is within an absolute error tolerance of 0.01. The advantage of this method lies in the speed of convergence compared to other numerical methods, such as the Division by Two method. In addition, sensitivity analysis was carried out to assess the effect of parameter changes on the calculation results. This article concludes with a discussion of the potential applications of the Regula Falsi method in other chemical fields, including the optimization of reaction processes on an industrial scale. With this approach, it is hoped that the Regula Falsi method can be an effective tool to support data-based decision-making in chemical research and process technology.
Stock Price Prediction with Mathematical Model Based on Secant Method Nabila, Andini Dara; Angelia
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.102

Abstract

Stock price prediction is a complex problem involving various factors, including market volatility and historical data. This study proposes a mathematical model based on the secant method to predict stock prices. The secant method, as a simple but effective numerical algorithm, is used to approximate nonlinear solutions to stock price trends. Historical stock data is analyzed to form a function that represents the pattern of price changes. This function is the basis for applying the secant method to predict stock prices at a certain time. The study was conducted using stock data from several companies, with performance evaluation based on the level of prediction error compared to actual data. The results show that the secant method is able to produce predictions with a low average error rate and high computational efficiency. This makes it an attractive choice compared to more complex models, especially in resource-constrained environments. However, accuracy decreases in highly volatile market conditions, indicating the need for further development. This method offers a simple yet reliable approach to stock price prediction, so it can be used as a tool for investors or market analysts, taking into account its limitations.  
Predictive Maintenance Strategies for Industry 4.0: A Modelling Approach Subagja, Asep; Watanto, Gunawan; Mujadi, Agus
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.121

Abstract

The advent of Industry 4.0 has revolutionized industrial operations by integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics into manufacturing systems. Among its many applications, predictive maintenance emerges as a critical strategy to minimize downtime, reduce operational costs, and enhance asset longevity. This article presents a modelling approach to predictive maintenance tailored for Industry 4.0 environments. We explore how real-time data acquisition and machine learning algorithms can be integrated into a predictive maintenance framework, enabling early fault detection and optimal scheduling of maintenance activities. The study proposes a comprehensive model that incorporates sensor data analysis, failure prediction, and decision support systems. Simulations and case studies demonstrate the effectiveness of the proposed approach in increasing system reliability and efficiency. Our findings highlight the pivotal role of data-driven models in transforming traditional maintenance practices into proactive, intelligent maintenance strategies suitable for smart factories.
A Survey on Object Detection in Dynamic and Complex Environments Soni, Ritu; Kumar, Ravi; Jain, Sheetal
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.134

Abstract

Object detection has become a cornerstone of computer vision, with applications ranging from autonomous driving and robotics to surveillance and augmented reality. While substantial progress has been made in controlled and static settings, real-world environments often pose significant challenges due to dynamic backgrounds, occlusions, illumination variations, and cluttered scenes. This survey provides a comprehensive review of recent advancements in object detection specifically tailored for dynamic and complex environments. We classify existing approaches based on their core methodologies, including traditional feature-based techniques, deep learning models, and hybrid frameworks. Key challenges such as real-time performance, adaptability to environmental changes, and robustness to motion are discussed in depth. Furthermore, we analyze benchmark datasets and evaluation metrics commonly used in this domain, highlighting their limitations and suggesting improvements. Finally, we explore emerging trends and future directions, including the integration of spatiotemporal modeling, sensor fusion, and domain adaptation strategies. This survey aims to serve as a valuable reference for researchers and practitioners seeking to develop or apply object detection systems in real-world, unpredictable environments.
Exploring Emerging Trends in AI-Driven Technological Advancements Patil, Arya; Yadav, Shweta; Pandey, Manish
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.140

Abstract

The rapid evolution of Artificial Intelligence (AI) has catalyzed a transformative wave across diverse technological landscapes. This study explores emerging trends in AI-driven technological advancements with a focus on developments within the Indian context. Through a mixed-methods approach combining literature analysis, expert interviews, and case studies from India's tech ecosystem, this research identifies key innovation patterns, adoption drivers, and sector-specific applications of AI technologies. Findings reveal significant momentum in areas such as healthcare diagnostics, smart agriculture, fintech automation, and personalized education, fueled by governmental initiatives, startup growth, and increased academic-industry collaboration. Additionally, the study highlights the challenges of ethical governance, data privacy, and digital divide that accompany rapid AI integration. By mapping the trajectory of AI's evolution in India, this research contributes to a deeper understanding of global AI dynamics and offers strategic insights for policymakers, researchers, and technology developers worldwide.

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