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ARRUS Journal of Mathematics and Applied Science
Published by Yayasan Ahmar Cendekia Indonesia
ISSN : 27767922     EISSN : 28073037     DOI : https://doi.org/10.35877/mathscience.v1i1
Core Subject : Science, Education,
Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Mathematics Physics
Aim: To drive forward the fields related to Applied Sciences, Mathematics, and Its Education by providing a high-quality evidence base for academicians, researchers, scholars, scientists, managers, policymakers, and students. Scope: The focus is to publish papers that are authentic, original, and plagiarism free and should in interest of society and the world.
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol. 5 No. 2 (2025)" : 5 Documents clear
The Analysis Of Mathematical Connection Ability In Two-Variabel Linear Equation System Based On Self Regulated Learning Of Students In VIII Grade Of Mts Negeri 1 Kota Makassar Musfira, Nur Fadillah; Arsyad, Nurdin; Rusli, Rusli; Musa, Hastuty; Rahman, Abdul
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4088

Abstract

This study aims to analyze students' mathematical connection skills based on self-regulated learning in solving math problems in the Two-Variable Linear Equation System material. This research is qualitative research with a descriptive approach. The subjects in this study were class VIII MTs Negeri 1 Makassar City consists of two students for each level of high, medium, and low self-regulated learning. The research instrument used consisted of the main instrument, namely the researchers, and also the supporting instruments, namely a self-regulated learning questionnaire, a mathematical connection ability test, and an interview guide. The results showed that: (1) subjects who had high self-regulated learning met three indicators of mathematical connection, namely being able to recognize and use ideas in mathematics and understand the interrelationships of these ideas, and being able to recognize and apply mathematics in contexts in other fields of study. And able to relate mathematics in daily life. (2) subjects who have moderate self-regulated learning only meet two indicators of mathematical connections, namely being able to recognize and use ideas in mathematics and understand the interrelationships of these ideas, and being able to recognize and apply mathematics in the context of daily life. (3) Subjects who have low self-regulated learning, namely subjects R1 and R2 were unable to fulfill the three indicators of mathematical connection. Subject R1 was only able to fulfill one indicator of mathematical connection, while subject R2 did not fulfill any indicator of mathematical connection.
Implementation of Support Vector Regression (SVR) and Double Exponential Smoothing (DES) for Forecasting BRI Stock Prices Meliyana, Sitti Masyitah; Aidid, Muhammad Kasim; Rahmadhani, Amaliyah
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4282

Abstract

This study aims to forecast the closing stock prices of BRI using Support Vector Regression (SVR) and Double Exponential Smoothing (DES) methods. The data used in this research is secondary data obtained from the Yahoo Finance website, covering the period from January 2020 to November 2023. The analytical steps using the SVR method involve selecting the optimal model by applying Grid Search Optimization to various kernels (linear, polynomial, radial, and sigmoid). The best-performing model was found to be the radial kernel with parameters ? = 0.1, C = 100, and ? = 10, yielding a Mean Absolute Percentage Error (MAPE) of 0.2431%, which was then used for forecasting. For the DES method, the steps involved parameter determination and minimizing the MAPE value, followed by smoothing calculations and forecasting. The optimal parameters obtained were ? = 0.89 and ? = 0.01, resulting in a MAPE value of 1.4832%. Based on the comparison of MAPE values, it can be concluded that the SVR method with a radial kernel (? = 0.1, C = 100, ? = 10) provides the most accurate forecasts for BRI closing stock prices, with the lowest MAPE of 0.2431%.
Development of Project-Based Mathematics Module to Enhance 21st Century Skills of Junior High School Students Lince, Ranak; Husnaeni, Husnaeni; Rustam, Rustam
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3809

Abstract

This study aims to develop a project-based mathematics module to enhance 21st century skills of junior high school students. The ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model was employed in this research. The results indicate that the developed module meets highly valid criteria based on expert validation: content expert (90%), media expert (89%), and language expert (97%). The practicality test involving 9 mathematics teachers shows a percentage of 93% (very practical), while the practicality test with 30 students demonstrates positive results in the very practical category. The module is designed in accordance with the Independent Curriculum (Kurikulum Merdeka) and integrates project-based learning to develop students' critical thinking, creative thinking, problem-solving, communication, collaboration, and digital literacy skills.
A Hybrid Neural Network Approach Using SOM and LVQ for Mapping Crime Clusters in Indonesia Rais, Zulkifli; Meliyana, Sitti Masyitah; Hasbullah, Dinda Warfani
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4782

Abstract

Crime ratehigh crime rates in Indonesia are one of the important issues that need to be addressed with data-based strategies. This study aims to group provinces in Indonesia based on crime patterns using Self-Organizing Map (SOM) and classify the results using Learning Vector Quantization (LVQ). The results of the clustering analysis using SOM show that the optimal number of clusters is two, as supported by validation using Connectivity, Dunn Index, and Silhouette Score. Cluster 1 consists of 31 provinces with lower crime rates, while Cluster 2 includes 3 provinces with higher crime rates. To improve understanding of the clustering results, classification was carried out using the LVQ method, which produced an accuracy of 91.43%.
Classification of Family Welfare Card Recipients in Makassar City Using Decision Tree Algorithms Rais, Zulkifli; Fahmuddin S, Muhammad; Musfira, Musfira
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4783

Abstract

This study aims to analyze the factors influencing the determination of recipients of the Family Welfare Card (KKS) program in Makassar City and evaluate the level of accuracy of the decision tree model in the classification process. The KKS program is a government effort to accelerate poverty alleviation, so it is important to ensure that the selection process for program recipients is carried out on target. The decision tree method is used in this study because of its ability to simplify the decision-making process through an easy-to-understand tree structure. This study utilizes KKS recipient data with various variables, such as income, number of dependents, employment status, asset ownership, and education level, to build a classification model. The results of the study indicate that the variable of the Head of Household's (KRT) Highest Education Level (X4) has the highest level of importance in determining KKS recipients, followed by the variable Number of Family Members (X1), and the variable Ownership of Residential Buildings (X5). The decision tree model that was built has an accuracy level of 84.21%, which states the model's ability to classify KKS recipients effectively. This study also provides insight into the description of factors influencing KKS receipts, which can be used as a basis for formulating more efficient and targeted policies.

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