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International Journal of Applied Mathematics and Computing.
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : -     EISSN : 3047146X     DOI : 10.62951
Core Subject : Science, Education,
Computer Science & IT Mathematics
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. This journal is a peer-reviewed and open access journal of Mathematics and Computing
Arjuna Subject : Matematika - Matematika Komputer
Articles 23 Documents
 1   2   3 
A Hybrid Optimization Approach for Non Linear Function Approximation in High Dimensional Spaces Achmad Rifai; Sesi Herawani; Mery Windya Pramita
International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i1.1

Abstract

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.
Efficient Parallel Algorithms for LargeScale Matrix Factorization in Collaborative Filtering Systems Novi Siti Juariah; Rizky Pratama .H; Melda Ayu Nengsi
International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i1.2

Abstract

Collaborative filtering systems rely heavily on matrix factorization techniques, which often face scalability issues when handling large datasets. This paper presents an efficient parallel algorithm that leverages distributed computing to perform largescale matrix factorization. Experimental results show that our algorithm significantly reduces computation time while maintaining high accuracy. The approach has practical implications for recommendation systems, particularly in ecommerce and social media platforms.
A Comparative Analysis of Machine Learning Models for Predictive Analytics in Finance Jose Miguel Reyes; Lea Patricia Santos; Antonino Perez
International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i1.3

Abstract

This paper compares various machine learning models in their ability to predict financial trends, with a focus on time-series analysis. We evaluate models such as linear regression, decision trees, support vector machines, and deep learning, measuring their performance based on accuracy, computational cost, and interpretability. Our results reveal that deep learning models offer superior accuracy but are less interpretable, while simpler models, though less accurate, provide better insight into the underlying data. This research provides guidelines for selecting suitable models based on specific financial applications.
Numerical Solution of Partial Differential Equations for Heat Transfer in Composite Materials Carlos Alberto Gonzalez; Juan Felipe Sanchez; Mariana González Silva
International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i1.4

Abstract

This paper presents a numerical solution approach for solving partial differential equations (PDEs) that describe heat transfer in composite materials. Using finite element analysis (FEA), we analyze temperature distribution and thermal gradients within various composite configurations. The results demonstrate that our numerical solution approach accurately predicts temperature behavior, providing insights for materials engineering and design. This method is particularly useful for optimizing thermal properties in engineering applications involving multilayer materials.
Stochastic Differential Equations in Population Dynamics: Modeling and Analysis Saeful Ihsan; Intan Khoirun Nisa; Ahmad Jamaludin
International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i1.5

Abstract

This study explores the application of stochastic differential equations (SDEs) in modeling population dynamics. By introducing randomness into differential equations, we capture the inherent uncertainties in environmental and genetic factors affecting population growth. We derive analytical solutions for specific cases and provide numerical simulations to demonstrate how SDEs enhance predictions in ecological modeling. Our findings suggest that stochastic models provide a more robust framework for understanding population fluctuations in uncertain environments.
Understanding And Enhancing Diversity In Generative Models Ahmad, Munir; Chohan, Muhammad Kamran; Qureshi, Muhammad Zarif; Hassan Gul
International Journal of Applied Mathematics and Computing Vol. 1 No. 2 (2024): April : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i2.16

Abstract

This research delves into the crucial aspect of diversity within generative models, exploring both its understanding and potential enhancement. Diversity in generative models refers to the ability of the model to produce a wide range of outputs that cover the variability present in the underlying data distribution. Understanding diversity is fundamental for assessing the quality and applicability of generative models across various domains, including natural language processing, computer vision, and creative arts. We discusses existing methods and metrics for evaluating diversity in generative models and highlights the importance of diversity in promoting fairness, robustness, and creativity. It explores strategies for enhancing diversity in generative models, such as regularization techniques, diversity-promoting objectives, and novel architectures. By advancing our understanding of diversity and implementing techniques to enhance it, generative models can better capture the complexity and richness of real-world data, leading to improved performance and broader applicability.
Analyze The Effectiveness Of Dynamic Programming In Improving Robust Queue Management Strategies Hasanain Hamed Ahmed
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.22

Abstract

This Article Review for aims to analyze the effectiveness of dynamic programming as a tool to improve robust queue management strategies in service systems. Dynamic programming is an optimization technique used to determine the optimal solution to problems that can be broken down into smaller problems. Explore how dynamic programming can be used to improve queue management strategies, including reducing wait times, improving resource allocation, and increasing system efficiency. The research is based on an analytical model that combines dynamic programming with row theory Immune-waiting, includes mathematical and experimental analysis to evaluate the effectiveness of these strategies in different applied contexts. The research aims to provide practical insights on how dynamic programming can be used to improve the performance of SOA systems and to provide recommendations for improving management strategies.
Two Stage Lasso in Principal Component Analysis With an Application Afraa A. Hamada
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.26

Abstract

This paper will employ a novel approach that builds upon the lasso method, utilizing it in two stages.   The first stage applies to the principal components to select the important principal component and exclude the unimportant ones. This technique is effective in identifying significant principal components while attempting to eliminate bias in selecting these components over others. Additionally, it removes the ranking in determining the principal components compared to classical methods.  Moreover, the second stage involves determining the effective importance within each component by zeroing out the scores loading values within each component. To compare the performance of the proposed method in principal component analysis, a simulation approach can be used. Subsequently, the performance of the proposed method is tested using real data.
Factors That Influence Diabetes Disease: Case Study: Pima Indians Ni Made Deviani Prisilia; Adelia Yuniarti; Citra Annisa Rahmania; Made Ayu Asri Oktarini Putri; Made Susilawati
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.27

Abstract

Diabetes is one of the non-communicable diseases that is considered dangerous due to its susceptibility to complications. This disease is caused by high blood sugar levels in a person's body, which makes the blood more alkaline and slows down the metabolic process. In this study, we observed 8 variables that are considered influential in diabetes and will build a regression model that can predict the response variable (y) through Logistic Regression Analysis. Logistic Regression Analysis is a statistical analysis method used to describe the relationship between a dependent variable with two or more categories and one or more independent variables that are categorical or continuous. Based on the results, the logistic regression model for factors influencing diabetes in the Indian Pima tribe includes variables such as number of pregnancies, glucose level, blood pressure, body mass index, and diabetes pedigree function
Image Classification Comparison Using Neural Network and Support Vector Machine Algorithm With VGG16 As Feature Extraction Method Aulia Wicaksono; I Putu Eka Nila Kencana; I Wayan Sumarjaya
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.29

Abstract

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.

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