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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
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.
Dynamics of Interaction of One Prey and Two Competing Predators with Population Heterogeneity Babu, Raveendra; Wani, Imtiyaz Ahmad
Indonesian Journal of Mathematics and Applications Vol. 2 No. 2 (2024): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2024.002.02.2

Abstract

A mathematical model of two predators competing for a single prey system incorporating population heterogeneity has been proposed and analyzed by extending the system of coupled logistic equations for three species system. The local stability analysis of all the equilibria has been carried out and also globally stable in Varies 2-D planes. Through the analytical results, it is observed that the interior equilibrium of one prey and two competing predators in prey dependent case is always unstable. At the end, discussed the dynamical behavior of a three species interaction with the help of some numerical examples by using MATLAB to support the analytical presentations.
Approximate and Exact Solution of Linear and Nonlinear Schrödinger Equation using Sawi Transform coupled with Homotopy Perturbation Method Olubanwo, Oludapo O.; Adepoju, Julius; Ajani, Abiodun Sufiat; Idowu, Senayon Sunday; Ifeyemi, Opeyemi
Indonesian Journal of Mathematics and Applications Vol. 2 No. 2 (2024): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2024.002.02.1

Abstract

This study presents an approximate solution using the Sawi Transform Method for the Schrödinger equation. Four problems were considered to illustrate the Sawi Transform Homotopy perturbation Method’s (STHPM) effectiveness and capabilities. The equation below is subjected to the Sawi Transform combined with Homotopy Perturbation Method (STHPM). $i\frac{\partial}{\partial t}\\psi(z,t)+\Delta[\psi(z,t)]+N\psi(z,t)=0$ The results obtained are represented in a series of rapidly convergent terms.
Morphisms and Algebraic Points on the Quotients of the Fermat Quintic Fall, Moussa
Indonesian Journal of Mathematics and Applications Vol. 2 No. 2 (2024): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2024.002.02.4

Abstract

The purpose of this paper is to determine morphisms and algebraic points of low degree on the quotients of the Fermat quintic. An algebraic point of degree at most $3$ is called an algebraic point of low degree. The quotients of the Fermat quintic is a family curves $\mathcal{C}_{rs}(5) : v^5 =u^r(u-1)^s$ where $r$ and $s$ are integers such that: $1< r, s, r+s< 5$. We determine explicitly the morphisms between the curves $\mathcal{C}_{rs}(5)$. By applying Abel Jacobi's theorem and using Riemann-Roch spaces, we give a parametrization of algebraic points of low degree on the special curve $\mathcal{C}_{1,1}(5)$. Birational morphisms allow us to determine the set of algebraic points of low degree on each curve of the family curves $\mathcal{C}_{rs}(5)$.
Stability Analysis of l-Power Reciprocal Functional Equation in Intuitionistic Fuzzy Banach Spaces Sadani, Idir
Indonesian Journal of Mathematics and Applications Vol. 2 No. 2 (2024): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2024.002.02.5

Abstract

In this study, we explore the generalized Hyers-Ulam-Rassias stability of a l-power reciprocal functional equation in Intuitionistic Fuzzy Banach Spaces.
Ring of Weight Enumerator of Integer Azaliyah, Syarifatul; Hamid, Nur; Alghofari, Abdul Rouf; Anam, Syaiful
Indonesian Journal of Mathematics and Applications Vol. 2 No. 2 (2024): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2024.002.02.3

Abstract

 Invariant theory can be combined with coding theory to analyze the polynomial ring over its weight enumerator. For a certain class code, especially for self-dual doubly even code or type II code, the polynomials over its code satisfy the invarian condition. In this paper, we will  show that the ring of the weight enumerator over integer of self-dual doubly even code $d_{48}^+$ cannot be ganerated by the weight enumerator of self-dual doubly even code with code length n=8, 16, 24, 32 and 40. 
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
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
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
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
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.

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