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
20 Documents
<![CDATA[Application of Cluster Analysis and Correlation between Mathematics and Natural Sciences Subject Based on Student Test Scores Using K-Means Clustering ]]>
<![CDATA[Pangesti, Sekar]]>;
<![CDATA[Roslan, Nurul Farisah Binti]]>;
<![CDATA[Andreansyah]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i1.8
<![CDATA[Educational unit examinations are a means of supporting decisions to determine students' abilities in mastering all the material that has been taught. education unit examinations also have an impact on students' ability to continue to the next level of education, besides that they can also improve the quality of school education. Based on the background of the problem, this researcher aims to group learning outcomes for mathematics and natural sciences subjects based on educational unit exam scores using the k-means clustering algorithm, in addition to finding out the relationship between mathematics and natural sciences subject learning outcomes using Spearman rank correlation analysis. The population in this study was all 39 students in class IX of SMP Plus Berkualitas Lengkong Mandiri for the 2023/2024 academic year. The sample in this study used saturated sampling. The research results show that through clustering using the k-means clustering algorithm, it was found that cluster 1 with the high category had 21 students, cluster 2 with the medium category had 12 students and cluster 3 with the low category had 6 students. Based on the Spearman rank correlation analysis of the entire sample, a low correlation was obtained between mathematics results and natural sciences results based on educational unit exam scores of 0.608 and a coefficient of determination of 36.9%. Meanwhile, the Spearman rank correlation test on all cluster 1 samples showed a moderate relationship between mathematics learning outcomes and natural sciences subject learning outcomes of 49.5%.]]>
<![CDATA[Application of Number Theory in the Guess Year of Birth Game ]]>
<![CDATA[Hermawan, Aldi]]>;
<![CDATA[Kurniadi, Edi]]>;
<![CDATA[Sylviani, Sisilia]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i1.15
<![CDATA[Number theory is a field of mathematics in which numbers are discussed. There are many developments in this theory. Among them are divisibility, modulo, inverse modulo, congruence and Chinese Remainder Theorem. This research focuses on divisibility and the Chinese Remainder Theorem. What is explained in more detail is a study of several divisibility properties and the application of the Chinese Remainder Theorem to the birth year guessing game. This game is quite popular among middle and high school students. Then, it also explains the technicalities and formulas used in the game to be able to guess the number or year of birth in question. The main finding of these work are prove several divisibility properties and the application of the Chinese Remainder Theorem in the guess year of birth game.]]>
<![CDATA[Implementation of Combination RSA, Vigenere Cipher, and Permutation Cipher in Digital Communication ]]>
<![CDATA[Tielung, M. Fariel Fahriza]]>;
<![CDATA[Kurniadi, Edi]]>;
<![CDATA[Sylviani, Sisilia]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i1.18
<![CDATA[In the digital era, data security is essential for preventing unwanted access to information. To improve message security, this study intends to apply a mixture of three cryptographic algorithms: RSA, Vigenere cipher, and permutation cipher. The Vigenere and permutation ciphers are used to the message content to add an extra degree of security, while the RSA technique is utilized for asymmetric key encryption. In this system, the Vigenere cipher and permutation cipher, is used to encrypt the message, followed by RSA is used to encrypt the Vigenere process key. According to test results, combining these three techniques strengthens the cryptographic defenses against frequency analysis and brute force assaults by increasing their complexity. Although the combination lengthens the processing time, the resulting security is higher than the application of a single method. This system is expected to be applied to applications that require the protection of sensitive data with a high level of security.]]>
<![CDATA[ARIMA Model for Forecasting COVID-19 Positivity Rate in Jakarta ]]>
<![CDATA[Wardhani, Andreanne Intan Sulistyo]]>;
<![CDATA[Ulya, Aulia Himmatul]]>;
<![CDATA[Febrianti, Ranny]]>;
<![CDATA[Suandi, Dani]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i1.20
<![CDATA[COVID-19, a global pandemic since early 2020, has affected numerous countries worldwide, including Indonesia. The positivity rate serves as a crucial metric for gauging the extent of COVID-19 transmission in a specific region. The surge in COVID-19 cases in Jakarta has notably impacted various industries, particularly the healthcare sector. In this paper, ARIMA model is used to predict the number of daily COVID-19 cases in Jakarta. The optimal model for predicting the positivity rate over the next 10 days, from June 22, 2022, to July 1, 2022, is identified as ARIMA (1,1,3). The forecasting error is quantified by the MAE of 0.5625, MSE of 0.0161, RMSE of 0.1268, and MAPE of 0.68% all of which attest to its high accuracy. From a mathematical perspective, the outcomes of this study offer advantages by elucidating the utilization of the ARIMA technique for forecasting time series data, particularly in scenarios involving disease spread, necessitating meticulous management. Additionally, within the healthcare domain, these findings offer valuable insights into endeavors aimed at controlling infectious diseases, particularly COVID-19, should similar outbreaks occur in the future.]]>
<![CDATA[Web Application for IHSG Prediction Using Machine Learning Algorithms ]]>
<![CDATA[Wijaya, Andryan Kalmer]]>;
<![CDATA[Lucky, Henry]]>;
<![CDATA[Arifin, Samsul]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i1.21
<![CDATA[This study investigates the effectiveness of the Long Short-Term Memory (LSTM) method in predicting the stock price of the Composite Stock Price Index (CSPI). LSTM, a variant of Recurrent Neural Networks, is designed to overcome challenges such as the vanishing gradient problem and long-term dependencies in time-series data. Given the dynamic and volatile nature of financial markets, accurate stock price prediction is crucial for investors and analysts. The data set used in this study consists of daily CSPI prices from January 2000 to December 2023, which serve as both training and testing data for model development. The LSTM model is trained to forecast the next day’s stock price, and its performance is compared with traditional statistical models, particularly the Autoregressive Integrated Moving Average (ARIMA) model and linear regression. Performance evaluation is based on the Mean Absolute Percentage Error (MAPE), a widely used metric for assessing predictive accuracy. The results indicate that while the ARIMA model achieves a lower MAPE of 0.7%, demonstrating slightly superior accuracy, the LSTM model also performs well, with a MAPE of approximately 1%. These findings suggest that while statistical models like ARIMA remain highly effective for stock price forecasting, deep learning approaches such as LSTM still offer promising predictive capabilities, especially when handling large and complex datasets. The ability of LSTM to capture non-linear patterns and temporal dependencies makes it a viable alternative for financial forecasting, potentially benefiting traders and market analysts seeking data-driven decision-making tools.]]>
<![CDATA[Clustering Analysis: A Note on Methodologies and Trends ]]>
<![CDATA[Raditha, Alya Maura]]>;
<![CDATA[Arifin, Samsul]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i2.23
<![CDATA[This study conducts a bibliometric analysis of clustering techniques in scientific research using VOSviewer and Gen-AI-based Consensus.app. The dataset was collected from Scopus and the Web of Science using predefined queries to filter articles published in 2024 and 2025. VOSviewer was utilized to visualize co-authorship networks, keyword co-occurrence, citation relationships, bibliographic coupling, and co-citation patterns, revealing key research clusters and influential studies. Additionally, Consensus.app was employed to generate AI-driven insights, summarizing key themes and emerging trends in clustering methodologies. The results indicate that clustering research is highly collaborative, with strong institutional networks and interdisciplinary applications. Machine learning, data mining, and network analysis emerge as dominant themes, with key publications shaping methodological advancements. The co-citation network highlights foundational studies that have influenced the field. By combining traditional bibliometric techniques and AI-based analysis, this study offers a comprehensive perspective on clustering research, identifying knowledge gaps and potential future directions. These findings provide valuable insights for researchers seeking to explore emerging topics, collaborate effectively, and contribute to the development of clustering methodologies. However, this study is limited to publications indexed in Scopus and Web of Science within the years 2024–2025, which may not fully capture longer-term developments. Future research could expand the scope to other databases and timeframes for a broader perspective.]]>
<![CDATA[An Exploration of Principal Component Analysis using Module Theory ]]>
<![CDATA[Suharto, Istiqomah]]>;
<![CDATA[Sylviani, Sisilia]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i2.25
<![CDATA[With big data becoming more and more prominent in the world, high-dimensional data is a relevant issue due to the challenges that it imposes to meaningful analysis. The exponential growth in space leads to data becoming sparse thus making it difficult to analyze underlying patterns/relationships. This is where dimension-reducing techniques come in, the most popular one being Principal Component Analysis. Hence, it is important to analyze exactly what makes PCA work and to study it and generalize it in order to leave room for more variants for differing fields and applications to grow. This paper examines PCA from a linear algebra perspective, particularly using module theory. We prove that PCA is an module homomorphism, and, when all principal components are kept, an module automorphism, meaning that it preserves structure and is invertible. We then look into what happens algebraically when only a subset of principal components are kept. The module homomorphism is then only an module epimorphism, not an isomorphism, still structure preserving but not invertible. Through these findings, we find that there are three essential, algebraic properties of Principal Component Analysis, namely (1) the transformation must be linear, (2) it must project the data onto a new orthonormal basis, and (3) it must diagonalize the covariance (or correlation) matrix of the centered dataset. With these properties, we get an algebraic definition of PCA: an module automorphism that diagonalizes the covariance structure of the original dataset via an orthogonal change of basis.]]>
<![CDATA[Minimum Distance Computation Efficiency in Module Codes over Finite Chain Rings ]]>
<![CDATA[Meishrin, Ishlahrahmi]]>;
<![CDATA[Sylviani, Sisilia]]>;
<![CDATA[Achiaa, Amma]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i2.26
<![CDATA[In coding theory, the module structure can be used in the construction and analysis of codes for reliable data transmission. A code that is effective in detecting and correcting errors that occur during data transmission is a good code. The parameter to determine the reliability of the code in error detection and correction is by calculating its minimum distance. However, to calculate the minimum distance, it is necessary to evaluate all the Hamming weights of all non-zero code words, if the size of the code are very long, the calculation of the minimum distance will take a long time so it is not very efficient if we use this method. Therefore, the module structure of the ring, especially over the finite chain ring, can be used to simplify the calculation process of the minimum distance. The complexity of calculating the minimum distance can be reduced by viewing the code as a module and utilizing its modular structure. Previous research has shown that certain structural characteristics of module codes can significantly simplify this computation. In this article, we present an efficient method for determining the minimum distance of a module code defined over a finite chain ring. We show that it only required the hamming weight of the code generator to find the code’s minimum distance. This approach provide an efficient way to analyze the code’s reability and give algebraic insight about the code’s behaviour. Our article highlight the practical advantages of applying a module theory perspective to coding theory, which offers an efficient and theoretically grounded framework for evaluating and constructing error-correcting codes with strong performance guarantees.]]>
<![CDATA[K-Means Clustering for the Relationship Between Mathematical Problem Solving Ability and Mathematical Communication Ability ]]>
<![CDATA[Dermawan, Rendi]]>;
<![CDATA[Hidayat, Dayat]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i2.27
<![CDATA[The purpose of this research is to examine the relation between mathematical problem-solving ability and mathematical communication ability by applying k-means clustering in grouping students. This research uses a quantitative approach with a correlational research method. The population of this research is the eighth-grade students of SMP Negeri 3 Purwakarta, with a total of 355 students. The sampling technique uses simple random sampling and is calculated using the Slovin formula, so the total sample is 79 students. The instrument used in this research is a subjective test in the form of questions of mathematical problem-solving ability and mathematical communication ability. The data analysis technique in this research is k-means clustering to group the data and correlation hypothesis test. The results show that 37 students fall into Cluster 1 (high-ability), 28 students into Cluster 2 (medium-ability), and 14 students into Cluster 3 (low-ability). In all samples and cluster 1, it is stated that at the 95% confidence level there is a significant relationship between mathematical problem-solving ability and mathematical communication ability. However, in all samples, it shows a weak relationship and the contribution of mathematical problem-solving ability to mathematical communication ability is 9.22%, and vice versa. While in Cluster 1, it shows a medium relationship and the contribution of mathematical problem-solving ability to mathematical communication ability is 31.2%, and vice versa. In Cluster 2 and Cluster 3, the analysis indicates that although there is a relationship at the 95% confidence level, it is not statistically significant.]]>
<![CDATA[Necessary and Sufficient Conditions for a Finite Cyclic Group to be a Cyclic Module ]]>
<![CDATA[Cahyadi, Arya Raditya]]>;
<![CDATA[Sylviani, Sisilia]]>;
<![CDATA[Gyenin, Mary]]>
<![CDATA[ Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.71385/idjams.v2i2.28
<![CDATA[The structures of cyclic groups and cyclic modules are fundamental topics in abstract algebra. While their individual structures are well-established, the precise conditions that link them over an arbitrary ring are often not detailed in the literature. This paper aims to find the necessary and sufficient conditions for a finite cyclic group of order n to be a cyclic -module for an arbitrary ring . The method used in this research is by utilizing the fundamental theorem stating a relationship between an abelian group viewed as a -module and a ring homomorphism from to the ring of endomorphism of its group, which is isomorphic to . The result of this research is a theorem proving that a finite cyclic group of order can be endowed with the structure of a cyclic -module if and only if there exists a surjective ring homomorphism from to (the ring of integers modulo ). This finding establishes a clear connection between these algebraic structures.]]>
