Indonesian Journal of Applied Mathematics and Statistics
The main aim of the Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) is to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of applied mathematics and statistics. This is a broad-based journal covering all branches of mathematics, statistics and interdisciplinary research. The journal publishes original papers including but not limited to the following fields: Applied Probability, Applied Statistics, Approximation Theory, Combinatorics, Complexity Theory, Computability Theory, Computational, Control Theory, Cryptography, Dynamical Systems, Financial Mathematics, Fuzzy Logic, Game Theory, Graph Theory, Information Theory, Inverse Problems, Linear Programming, Mathematical Biology, Mathematical Chemistry, Mathematical Economics, Mathematical Physics, Mathematical Psychology, Mathematical Sociology, Mathematical Education, Statistical Education, Matrix Computations, Neural Networks, Nonlinear Processes in Physics, Numerical Analysis, Operations Research, Optimal Control, Optimization, Ordinary Differential Equations, Partial Differential Equation, Probability Theory, Statistical Finance, Stochastic Processes, Theoretical Statistics, Risk Models Prediction Models
Articles
25 Documents
<![CDATA[A Conceptual Summary of the Application of Free Modules in the Applied Mathematical Science ]]>
<![CDATA[Khairiah Rahmah Virda Sari]]>;
<![CDATA[Sisilia Sylviani]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 3 No. 1 (2026): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[PT Anugrah Teknologi Kecerdasan Buatan ]]>
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DOI: 10.71385/idjams.v3i1.24
<![CDATA[Modules are one of the topics studied in algebra, furthermore there are free modules which are a subtopic of the module itself. This literature aims to find out the application of free modules in various branches of mathematics, both explicitly described and implicitly discussed in previous literatures. So that, this research used the Systematic Literature review to find the applications of module theory, especially the application of free modules in previous research. There are applications of free modules in various fields that are quite often discussed in system control theory, representation theory, cryptography, and mathematical physics. This research reveals that free modules not only play a role in pure algebra theory, but also have practical applications in various other sectors of science. This research is useful for future research to find out the application of modules, especially free modules, to research them in more depth.]]>
<![CDATA[Comparative Analysis of ARMA and GARMA Models in Predicting the Blooming Dynamics of Rafflesia arnoldii in Bengkulu Province ]]>
<![CDATA[Amelliana Melasarrah]]>;
<![CDATA[Jose Rizal]]>;
<![CDATA[Winalia Agwil]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 3 No. 1 (2026): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[PT Anugrah Teknologi Kecerdasan Buatan ]]>
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DOI: 10.71385/idjams.v3i1.29
<![CDATA[Bengkulu Province, known as the Land of Rafflesia, is home to Rafflesia arnoldii, a rare and iconic flowering species. Understanding the temporal dynamics of its blooming frequency is essential not only for effective conservation planning but also for strengthening flora-based ecotourism initiatives. Time series forecasting has been widely applied to ecological and environmental data, with the Autoregressive Moving Average (ARMA) model being one of the most commonly used approaches. However, ARMA relies on the white-noise assumption, which is often violated in count data such as the number of Rafflesia arnoldii blooms, leading to reduced accuracy. To address this limitation, this study applies the Generalized Autoregressive Moving Average (GARMA) model, which accommodates non-Gaussian data from the exponential family, including Poisson and Negative Binomial distributions. The dataset consists of monthly records of blooming events collected by the Bengkulu Natural Resources Conservation Agency from January 2015 to December 2023. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Arctangent Absolute Percentage Error (MAAPE). Results show that GARMA(1,0) achieved RMSE = 2.424, MAD = 2.224, and MAAPE = 31%, while GARMA(0,2) achieved RMSE = 2.550, MAD = 1.483, and MAAPE = 26%. In contrast, ARMA(1,0) performed less effectively, with RMSE = 3.694, MAD = 2.676, and MAAPE = 36%. These findings demonstrate that GARMA provides more stable and accurate forecasts, effectively capturing the stochastic properties of count data without depending on residual normality. The study highlights the methodological superiority of GARMA over ARMA, offering both theoretical contributions to time series modeling and practical benefits for biodiversity conservation. By enabling more reliable predictions of Rafflesia arnoldii blooms, GARMA can inform conservation policies and support sustainable tourism strategies in Bengkulu Province.]]>
<![CDATA[Modeling Diabetes Mellitus Prevalence in West Java Using Negative Binomial Regression ]]>
<![CDATA[Elaiya Najla Oasis]]>;
<![CDATA[Nasywa Firdausi Chandra]]>;
<![CDATA[Taskya Prianggita Alfinayah]]>;
<![CDATA[Abdul Azis Abdillah]]>;
<![CDATA[Restu Arisanti]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 3 No. 1 (2026): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[PT Anugrah Teknologi Kecerdasan Buatan ]]>
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DOI: 10.71385/idjams.v3i1.31
<![CDATA[Diabetes Mellitus is a disease with a high number of sufferers in Indonesia. According to the West Java Provincial Health Office, the number of people with Diabetes Mellitus in West Java Province in 2022 reached 644,704 cases. The causative factor of Diabetes Mellitus is obesity, one of the triggers is food intake. Foods consisting of carbohydrates, protein, saturated fat, sugar, and fat in general can increase blood glucose levels and increase the risk of diabetes. This study aims to determine the role of food in the prevalence of Diabetes Mellitus in West Java. The method used was negative binomial regression analysis. The results obtained show that the level of consumption of fruits and vegetables, oils and fats, and nuts has a significant influence on the prevalence of Diabetes Mellitus in West Java. Public awareness to implement good eating behavior is very important to reduce the prevalence of Diabetes Mellitus in West Java.]]>
<![CDATA[Early Dyslexia Detection Using Deep Learning: Classifying Children's Handwriting with Convolutional Neural Networks ]]>
<![CDATA[Muhammad Imamul Caesar]]>;
<![CDATA[Aditya Prihandhika]]>;
<![CDATA[Jasem Al Tamar]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 3 No. 1 (2026): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[PT Anugrah Teknologi Kecerdasan Buatan ]]>
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DOI: 10.71385/idjams.v3i1.34
<![CDATA[This study investigates the development of an automated handwriting-based dyslexia classification model using a Convolutional Neural Network (CNN). The dataset comprises scanned handwriting samples from children diagnosed with dyslexia and those without learning difficulties. Prior to model training, the images were resized to a uniform dimension, converted to grayscale, and normalized to standardize pixel intensity values. Data augmentation techniques, including rotation, scaling, and horizontal shifting, were applied to increase data diversity and reduce overfitting. A lightweight CNN architecture was then employed to perform binary classification between dyslexic and non-dyslexic handwriting samples. Experimental results indicate that the proposed model achieved an accuracy of 51%, with a precision of 0.51 and a recall of 0.42, suggesting that its current predictive performance remains limited. These findings highlight the challenges of dyslexia classification using handwriting features alone, particularly when constrained by model simplicity and data resolution. Nevertheless, this study serves as an exploratory step toward automated dyslexia screening and provides insights for future work, where performance may be improved through the use of deeper network architectures, such as ResNet-18, and higher-resolution handwriting representations.]]>
<![CDATA[Foundations of Krein Spaces and the Nonequivalence of Pythagorean Orthogonality ]]>
<![CDATA[Bagas Reynalda]]>;
<![CDATA[Sisilia Sylviani]]>;
<![CDATA[Anita Triska]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 3 No. 1 (2026): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[PT Anugrah Teknologi Kecerdasan Buatan ]]>
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DOI: 10.71385/idjams.v3i1.35
<![CDATA[This paper presents a fundamental analysis of the Krein Space, which is an indefinite generalization of the Hilbert Space. The Krein Space (K, [·,·]) is equipped with an indefinite inner product [·,·], which satisfies all the properties of a standard inner product except for positive definiteness. The absence of the positive definite property allows for the classification of elements based on their indefinite inner product with themselves into positive, negative, and neutral elements. The Krein space is formally defined through a canonical decomposition K = K+ [Å] K-, where K+ and K- are indefinitely orthogonal subspaces, both of which become Hilbert Spaces after adjusting the sign of their indefinite inner product. This decomposition induces a Canonical Symmetry Operator J, which allows the indefinite inner product to be related to a definite inner product <·,·> through the relation <x,y> = [J x, x]. This relationship defines the induced Hilbert Space norm ll · ll on K, where ll x ll2 = [J x, x]. The main focus of this paper is to demonstrate that indefinite orthogonality in Krein Space fundamentally differs from Hilbert Space orthogonality, particularly concerning the Nonequivalence of the Pythagorean orthogonality.]]>
