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All Journal Fundamental Management Journal Jurnal Ilmu Hukum, Humaniora dan Politik (JIHHP) Jurnal Media Akademik (JMA) fundamental management journal International Journal of Technology and Modeling
Angelia
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : international journal of technology and modeling

 1 
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.
Implementing LU Decomposition to Improve Computer Network Performance Angelia; Bandiyah, Salza Nur; Marine, Yoni
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.101

Abstract

The application of LU decomposition in computer networks has great potential to improve system performance, especially in processing and analyzing complex and large-sized data. LU decomposition is a technique in linear algebra that breaks down a matrix into two triangular matrices, namely the lower (L) and upper (U) matrices, which facilitates the solution of a system of linear equations. In the context of computer networks, these algorithms can be applied to accelerate the analysis and processing of network traffic data, resource management, and traffic scheduling. Large matrices are often used to model networks in applications such as route mapping, bandwidth allocation, and network performance monitoring. The use of LU decomposition allows efficiency in handling such big data, speeds up calculations and reduces latency time in network information processing. This study proposes the application of LU decomposition to optimize several aspects in computer networks, such as dynamic routing, network fault detection, and more effective resource allocation. With LU decomposition, the process of load analysis and problem identification can be carried out more quickly, increasing the throughput and stability of the system. The results of the experiments conducted show that the application of LU decomposition can reduce the computational load and accelerate the system's response to changes in network conditions. Overall, the application of these methods can contribute to improving the efficiency and performance of modern computer networks, especially in the face of increasingly high and complex data traffic demands.
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.  
Co-Authors Achmad Gusti Malayudha Bandiyah, Salza Nur Dinda Oktaviani Putrikusuma Eugenia Desta Riana Putri Hidayat, Rafi Kamila Alfiana Rahme Lie, Gunardi Lumbantoruan , Rutman Marine, Yoni Nabila, Andini Dara Naimatussaufafida Ardia Liyahana Rila Setyaningsih Serafine Angelina Yudianto Sidharta , Juaniva