cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Saluky
Contact Email
etunaspublisher@gmail.com
Phone
+6289604331800
Journal Mail Official
etunaspublisher@gmail.com
Editorial Address
Jl. Pilang Gg. Sukajaya no 11 Rt/RW 02/10
Location
Kab. cirebon,
Jawa barat
INDONESIA
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
 1   2   3   4   5 
Polynomial Interpolation in Flight Schedule Planning Nurillathifah, Azsky Azkiyyatunnafsi; Hikmah, Nurul
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.v3i2.77

Abstract

Flight schedule planning is a crucial aspect in the air transportation industry to ensure operational efficiency and customer satisfaction. One of the mathematical methods that can be used in such planning is polynomial interpolation. This study aims to analyze the application of the polynomial interpolation method in optimizing flight schedules, especially to predict departure and arrival times based on historical data. Polynomial interpolation is used because of its ability to model non-linear relationships from a series of data points. In this study, the data used included actual flight times on a specific route over a specific period. The Lagrange and Newton interpolation method was applied to build a predictive model of flight schedules. The results show that polynomial interpolation can provide a fairly accurate prediction of flight time, with minimal deviation compared to the actual schedule. Additionally, this method helps in detecting frequent anomalies and delays, allowing for better schedule planning. However, computational complexity increases as the amount of data grows, which becomes a challenge in large-scale deployments. Thus, polynomial interpolation can be an effective tool in planning flight schedules, especially for airlines in improving punctuality and operational efficiency. This research is expected to contribute to the development of a decision support system in flight schedule management.
Predicting Air Pollution Using Simpson Integration Karmilah; Nazwa
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.v2i3.82

Abstract

Increasing air pollution, especially in urban areas, is a serious issue that has a negative impact on public health and the environment. Accurate prediction of air pollution levels is critical to supporting mitigation efforts and data-driven decision-making. This study aims to develop an air pollution prediction model using the Simpson Integration method, a numerical approach used to calculate integrals with a high degree of accuracy. The data used included concentrations of pollutants such as PM2.5, PM10, and NO2 taken from daily measurements for one year. This method utilizes an interpolation algorithm to model changes in pollutant concentrations as a function of time. Simpson integration is used to calculate the area under the daily pollutant curve that represents the accumulated exposure to air pollution. The results show that this method is able to provide accurate predictions with an average error rate of less than 5% compared to actual data. This model has advantages in computational efficiency over conventional methods such as simple linear regression analysis. These findings prove that Simpson Integration can be effectively applied in air quality prediction and provide important information for governments and the public. This system is expected to support the development of an air pollution early warning system to increase public awareness and help formulate more responsive environmental policies.
Simulating the Movement of Planets in the Solar System Using a Linear System Nurul Fadhilah; Muarief, Muchammad; Aningtias, Rizka Fitri
International Journal of Technology and Modeling Vol. 2 No. 1 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i1.95

Abstract

This article discusses the simulation of planetary movements in the solar system using a linear system-based approach. Mathematical models of solar systems often involve non-linear differential equations, which include the complexity of gravitational interactions between planets and other celestial bodies. However, to simplify the calculation and analysis process, a linear approach can be used with certain assumptions. In this study, the motion of the planets is modeled using Newtonian mechanical principles adapted into a linear equation system. The simulation is carried out by utilizing numerical computing software to calculate the position and speed of the planet in a certain time span. The simulation results show that the linear system approach is able to represent the basic motion of the planet with an adequate degree of accuracy on short time scales, but it shows limitations in predicting complex dynamics, such as orbital resonance or the gravitational influence of small bodies. This approach is suitable for educational applications, where visualization of planetary movements can help understand the basic principles of orbital dynamics. These findings emphasize the importance of choosing the right simulation method according to the purpose, both for scientific and educational purposes. The study suggests the development of a hybrid model that combines a linear approach with non-linear elements to improve accuracy without losing computational efficiency.  
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.
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.  
Root Search Applications in Electric Vehicle Cooling Systems Lisa, Ade; Anika
International Journal of Technology and Modeling Vol. 2 No. 1 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i1.103

Abstract

Electric vehicle cooling systems are crucial components that maintain the performance and life of batteries and electric motors through temperature regulation. This research aims to develop a mathematical root search-based application used in the optimization of electric vehicle cooling systems. The methodology used involves mathematical modeling of thermal and fluid flows in a cooling system, followed by the implementation of numerical methods, such as Newton-Raphson and Bisection, to solve non-linear equations related to cooling efficiency. The results show that this application is able to identify optimal operating parameters, such as fluid flow velocity and heat distribution, with high accuracy and efficient computing time. The conclusion of this study confirms that mathematical root search can be applied effectively in the design and operation of electric vehicle cooling systems. The contribution of this research to science includes the development of a systematic approach based on numerical algorithms that can be integrated in thermal simulation software for electric vehicles, thereby supporting innovation in the field of environmentally friendly transportation.
Gauss's Elimination to Solve Financial Modeling Models in Banks Dwi Oktavianty, Firda; Inayatussulaimah; Hardianti, Siti
International Journal of Technology and Modeling Vol. 1 No. 3 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i3.105

Abstract

Gauss's elimination is an effective mathematical method for solving linear equation systems and is widely applied in various fields, including financial modeling. This article aims to apply Gauss's elimination method in solving complex financial modeling models in banks, especially in credit portfolio analysis and risk management. This study uses a quantitative approach by applying Gauss's elimination to bank financial data, involving a linear equation system that represents the relationship between risk factors, credit interest, and payment capacity. The results of the analysis show that this method is able to provide an efficient and accurate solution in determining the optimal combination of credit portfolios and minimizing default risk. The simulation also confirmed the reliability of Gauss's elimination in handling large-scale data with a variety of financial parameters. The conclusion of the study is that Gauss's elimination is not only relevant in a theoretical context but also highly applicable in the banking industry to improve data-driven decision-making. The contribution of this research to science is to provide an innovative approach to utilize classical mathematical methods in solving modern problems in the financial sector, as well as to provide a basis for further research in the field of linear equation-based financial modeling.
Population Dynamics Modeling with Differential Equation Method Zahra Rustiani Muplihah; Dede Nurohmah; Marine, Yoni; Hidayat, Rafi
International Journal of Technology and Modeling Vol. 1 No. 3 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i3.107

Abstract

Population dynamics modeling is one of the important approaches in understanding population development and its influence on various aspects of life, such as economic, social, and environmental. This article discusses the application of differential equation methods in modeling population dynamics, with a focus on the analysis of growth and interactions between populations. The models used include exponential growth models, logistics, and the Lotka-Volterra model to describe competitive interactions and predations between populations. Through numerical simulations and qualitative analysis, this article shows how parameters such as birth rate, mortality, and environmental carrying capacity affect population growth patterns. In addition, the influence of external factors such as government policies and natural disasters is also incorporated into the model to expand the application in real contexts. The results of the analysis show that the differential equation model is able to provide an accurate picture of population dynamics if the parameters are estimated correctly. This article also highlights the importance of model validation using empirical data to ensure prediction reliability. This modeling can be used as a tool in development planning, resource allocation, and risk mitigation in various sectors. The conclusion of this study is that the differential equation method is not only effective in explaining population phenomena, but also flexible to adapt to various dynamic conditions. As such, this approach offers a significant contribution to demographic studies and data-driven decision-making.

Page 2 of 6 | Total Record : 55

 1   2   3   4   5 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025) Vol. 4 No. 1 (2025) Vol. 3 No. 3 (2024) Vol. 3 No. 2 (2024) Vol. 3 No. 1 (2024) Vol. 2 No. 3 (2023) Vol. 2 No. 2 (2023) Vol. 2 No. 1 (2023) Vol. 1 No. 3 (2022) Vol. 1 No. 2 (2022) Vol. 1 No. 1 (2022) More Issue