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Agus Suryanto
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Faculty of Mathematics and Natural Sciences, Brawijaya University. Jl. Veteran, Malang City, East Java, Indonesia
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INDONESIA
Indonesian Journal of Mathematics and Applications
Published by Universitas Brawijaya
ISSN : -     EISSN : 29868149     DOI : https://doi.org/10.21776/ub.ijma
Core Subject : Science, Education,
Computer Science & IT Education Mathematics Other
The Indonesian Journal of Mathematics and Applications is a journal managed by Universitas Brawijaya, Malang, Indonesia, that is published twice a year (in March and September). IJMA is devoted to research articles of the highest quality in all areas of mathematics and its applications, statistics, and data science. The journal covers the following topics: Mathematical Analysis, Algebra, Biomathematics, Industrial Mathematics, Operasion Research, and Optimization, Data Sciences/Soft computing, Mathematical Physics, Financial Mathematics and Actuarial Sciences, Statictics. Upon its submission, the Editor-in-Chief decides on the suitability of the paper’s content for the aim and scope of the IJMA. If the Editor in Chief considers the paper is suitable, then the paper will be sent for peer reviewing by two peer reviewers. The Indonesian Journal of Mathematics and Applications maintains double anonymity, so neither the peer reviewers nor the author(s) can be identified by one another. The peer reviewers are respected scholars in the areas. The Indonesian Journal of Mathematics and Applications is an open access, peer-reviewed journal that considers articles describing novel computational algorithms and software, models and tools, including statistical methods, machine learning, and artificial intelligence, as well as systems biology.
Arjuna Subject : Matematika - Matematika Terapan
Articles 55 Documents
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Performance Comparison of Gradient-based Optimizer for Classification of Movie Genres Najib, Mohamad Khoirun; Irawan, Ade; Salsabilla, Fitra Nuvus; Nurdiati, Sri
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.1

Abstract

In this digital era, artificial intelligence has become very popular due to its very wide scope of application. Various models and methods in artificial intelligence are developed with their respective purposes. However, each model and method certainly requires a reliable optimizer in the training process. Many optimizers have been developed and are increasingly reliable lately. In this article, we classify the synopsis texts of several movies into nine different genre classes, leveraging Natural Language Processing (NLP) with Long Short Term Memory (LSTM) and Embedding to build models. Models are trained using several optimizers, including stochastic gradient descent (SGD), AdaGrad, AdaDelta, RMSProp, Adam, AdaMax, Nadam, and AdamW. Meanwhile, various metrics are used to evaluate the model, such as accuracy, recall, precision, and F1-score. The results show that the model structure with embedding, lstm, double dense layer, and dropout 0.5 returns satisfactory accuracy. Optimizers based on Adaptive moments provide better results when compared to classical methods, such as stochastic gradient descent. AdamW outperforms other optimizers as indicated by its accuracy on validation data of 89.48%. Slightly behind it are several other optimizers such as Adam, RMSProp, and Nadam. Moreover, the genres that have the highest metric values are the drama and thriller genres, based on the recall, precision and F1-score values. Meanwhile, the horror, adventure and romance genres have low recall, precision and F1-score values. Moreover, by applying Random Over Sampling (ROS) to balance the genre dataset, the model’s testing accuracy improved to 98.1%, enhancing performance across all genres, including underrepresented ones. Additional testing showed the model’s ability to generalize well to unseen data, confirming its robustness and adaptability.
Characterizing Linear Mappings Through Unital Algebra Atteya, Mehsin Jabel
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.4

Abstract

In this paper, we characterize the two linear mappings $\sigma$ and $\tau$ satisfying the identity, $x \circ y^{\star}=0$ yields $\sigma(x)\circ y^{\star}+x\circ \tau(y)^{\star}=0$ for all $x, y \in A$, where $A$ is an $\star$-algebra over a real or complex field $K$ from a unital algebra into its unital $\star$-bimodule. Moreover, we push a complete description of linear mapping that $\sigma$ is a linear mapping from $A$ into $M$ satisfying $X, Y \in A$, $X \circ Y=0$ yields $\sigma(X)\circ Y-X\circ \sigma(Y)=0$ and each element of $A$ has a weak inverse.
Integration of Almon and GARCH Methods to Overcome Heteroscedasticity Problems in Economic Time Series Analysis Oktariana, Salsa Agung; Putra, Muhammad Rafael Andika
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.3

Abstract

Homoscedasticity is an important assumption for statistical models, one of which is linear regression models from economic aspects which generally have time series type data. The Almon method is one approach used to handle lag effects in time series data. However, the residuals produced by the Almon method do not meet the assumption of homoscedasticity. To overcome this, it is necessary to handle the residuals from the Almon method using the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model. This research uses 4 different types of GARCH models. The GARCH (1,1) model is the most appropriate model as evidenced by the smallest BIC value, namely 18.19199. The result was that the GARCH (1,1) model could handle the heteroskedasticity problem in the Almon method residuals.
Comparison Study between Shooting and Finite Difference Methods for Solving Linear Boundary Value Problem with Dirichlet, Neumann, and Robin Boundary Conditions Ardiana, Dita; Rachman, Alifira Meliana; Nurkarimah, Dwi; Habibah, Ummu
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.2

Abstract

This study conducts a comparative analysis of the Shooting and Finite Difference methods for solving boundary value problems (BVPs) in ordinary differential equations (ODEs). The findings indicate that the Shooting method offers superior accuracy, particularly for smaller step sizes, whereas the Finite Difference method is more straightforward to implement and exhibits greater computational efficiency. The results further demonstrate that the Shooting method is particularly highly appropriate for problems with Dirichlet boundary conditions, as it achieves the lowest mean absolute error (MAE) across various step sizes. Conversely, the Finite Difference method attains higher computational efficiency for the same problem type but performs less advantageously in cases involving other boundary conditions. In contrast, the Shooting method demonstrates greater efficiency in solving problems with Neumann and Robin boundary conditions. The selection of an appropriate numerical method depends on the specific characteristics of the problem, necessitating a balance between accuracy and computational cost. This study provides a comprehensive evaluation of these numerical approaches to support the selection of the most suitable method for efficiently and accurately solving BVPs.
Enhancing Energy Consumption Forecasting with a Multi-Model Deep Learning Approach Fajri, Haidar Ahmad; Oleisan, Kirey
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.5

Abstract

 High energy consumption highlights the need for accurate primary energy forecasts to be critical for policy development, resource optimization and sustainable growth. Indonesia, the fourth largest energy-consuming country in Asia-Pacific, will face challenges in managing energy consumption for economic advancement if it does not conduct proper forecasts with large and limited data. Deep learning models, such as Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Transformer, excel at extracting insights and modelling temporal dependencies with minimal error, making them ideal for energy forecasting. The hybrid CNN-Bi-LSTM-Transformer model leverages complementary strengths: CNN captures initial patterns, Bi-LSTM manages temporal dependencies, and Transformer enhance global relationships. This model outperforms others model, including Linear Regression, CNN, Bi-LSTM, LSTM, CNN-LSTM, CNN-Bi-LSTM, CNN-Transformer, LSTM-Transformer, Bi-LSTM-Transformer, and hybrid CNN-LSTM-Transformer. It achieves a Mean Squared Error (MSE) of $\num{6.0006e-4}$ on train data, $\num{3.4485e-4}$ on test data and computation time of 8.20 minutes from 25 iterations, with 128 units of CNN layer, 150 units of LSTM layer, and four heads of attention in Transformer. The model also reports a Mean Absolute Error (MAE) of $\num{1.4000e-4}$ for training and $\num{1.5000e-4}$ test data and a Mean Absolute Percentage Error (MAPE) of $1.56$\% for train data and $1.75$\% for test data. This model also effectively tracks energy consumption trends with minimal fluctuations, accurately mirroring the original data and avoiding irregular variations, ensuring reliable future predictions in the long- and short-term.
Numerical Method for 1D Poisson-Boltzmann Equation Rizal Dian Azmi
Indonesian Journal of Mathematics and Applications Vol. 1 No. 2 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.02.4

Abstract

Poisson-Boltzmann is an equation which is used frequently to model a variety of electrostatic interactions problems in biomolecular computation. A value of atomic energy at the molecular movement becomes a singular value on the PBE. It affects the difficulty of solving this equation both analytically and numerically. In this article, this equation will be solved numerically. In this case, this equation is discretized using finite element method. The resulted system of equation is then solved with inexact backtracking Newton.
Some Basic Results in ν-Normed Spaces Rohman, Minanur; İlker Eryılmaz
Indonesian Journal of Mathematics and Applications Vol. 1 No. 1 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.01.1

Abstract

In this paper, we introduce the notion of a directed preserving generator (d.p.g.) from R into R. This d.p.g. can be used to construct new fields which generally have the same properties as R, except that some properties are affected by d.p.g. itself. With this new field, a ν-normed space will be formed. Some of the basic properties of this normed space are also discussed.
On the Inverse of a Square Matrix Using Logarithm Rellon, Louie Resti
Indonesian Journal of Mathematics and Applications Vol. 1 No. 1 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.01.2

Abstract

Assume that A is an n ‰ n matrix with real numbers as entries and the determinant of A ≠ 0. In this study, we established the results of getting the inverse of a matrix A using the logarithmic method and used some examples to discuss the necessity of getting the inverse of a matrix using the logarithm for both positive and negative determinants in terms of a given determinant of a matrix A. If the det A > 0, we have , where . However, if the det A < 0, we have , where .
Vector-Reservoir Transmission in A Japanese Encephalitis Model Lisa R. Sari; Puji Andayani; Sekarsari U. Wijaya
Indonesian Journal of Mathematics and Applications Vol. 1 No. 1 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.01.3

Abstract

In this paper, a model for characterizing the dynamics of vector-borne diseases is put out, emphasizing Japanese encephalitis. The susceptible-infectious-recovered (SIR) model for the host population and the susceptible-infectious (SI) model for the vector and reservoir populations are used to examine the role of host-vector-reservoir dynamics and their interplay. The standard incidence rate represents the probability of an actual disease contact. The model has two equilibrium points: an endemic equilibrium point that only exists under specific circumstances and a disease-free equilibrium point that always exists. The stability analysis of the model’s equilibrium point has been established. The basic reproduction number is calculated using the next-generation matrix method. A sensitivity analysis on models supported by numerical simulations is provided to demonstrate the critical parameter that affects the spread of disease.Our findings indicate that vector-reservoir transmission is the primary cause of endemic. Controlling vector-reservoir transmission lowers the likelihood of human infection and creates disease-free settings.
Application of Simulated Annealing Method on Tabarru-Fund Valuation using Inflator by Vasicek Model Approach Based on Profit and Loss Sharing Scheme Selvi Faristasari; Adhitya Ronnie Effendie
Indonesian Journal of Mathematics and Applications Vol. 1 No. 1 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.01.4

Abstract

Currently, the financial services industry is dominated by conventional banks and individuals that apply the system of interest or an excess of loans. In Islam, this excess is referred to as usury, which is prohibited by Islamic law because, in practice, usury makes borrowers poorer as they cannot pay such high-interest installments. Not to mention, late payments are subject to penalties that will continue to accumulate if the borrower is unable to pay the next installment. From these facts, this system is prohibited by Islamic Law because there are harmed parties. Therefore, this research discusses mathematical models in the form of Islamic investment business loans for micro-economic traders by implementing a profit and loss sharing system. Tabarru-fund is a set of funds derived from borrowers’ contributions used to overcome conditions when they experience losses in certain conditions. In this mathematical model, the tabarru-fund acts as the premium that must be paid if the borrower is still profitable after the principal installments have paid off. This sharia model with tabarru funds is obtained by calculating the premium which involves the problem of minimizing the remaining tabarru funds in a certain period. The future value of the trader's profit rate will be projected using the Vasicek Model approach which previously determined the parameter estimation using OLS regression and then the data is generated using Monte Carlo simulation so that the sharia inflator is obtained. This sharia inflator plays a role in the optimization process of minimizing the remaining tabarru-fund which will be solved by the Simulated Annealing (SA) algorithm.

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