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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 85 Documents
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Comparison of R-Forecasting and V-Forecasting Singular Spectrum Analysis in Forecasting Farmers' Exchange Rates in Indonesia Fahmuddin S., Muhammad; Ruliana; Ahmad Imdad
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 2 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3905

Abstract

Indonesia is an agricultural country where one of the main sources of income comes from the agricultural sector. One of the indicators often used to assess farmer welfare is the Farmer Exchange Rate (FER) index. Research on FER forecasting using the Singular Spectrum Analysis (SSA) method has been widely conducted, however, to date, there has been no research comparing the recurrent forecasting and vector forecasting methods in Indonesia. The purpose of this study is to obtain FER forecasting results using r-forecasting and v-forecasting, then compare the forecasting results based on MAPE values ​​to obtain the best forecasting results. The results of the study show that v-forecasting produces better forecasting results with a MAPE value of 0.57%. The forecast results for the next 12 months show an increase and decrease of FER in Indonesia. The highest FER value occurred in May 2022 at 103.79, while the lowest value was in September 2021 at 101.80.
Classification Of Hypertension Using Methods Support Vector Machine Genetic Algorithm (SVM-GA) Fahmuddin S, Muhammad; Rais, Zulkifli; Yuniar, Eka Citra
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3976

Abstract

Support Vector Machine (SVM) is a machine learning method for classifying data that has been successfully used to solve problems in various fields. The risk minimization principle used can produce an SVM model with good generalization capabilities. The problem with the SVM method is the difficulty in determining the optimal SVM hyperparameters. This research uses Genetic Algorithm (GA) to optimize SVM hyperparameters. GA optimization on SVM is used to classify hypertension. From the result of classification analysis using GA, it shows good accuracy value performance, namely 100% compared to using only SVM.
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.
Multimedia-Based Mathematics Teaching Materials for Eighth Grade Students at SMP Negeri 1 Dua Boccoe, Bone Regency Musa, Hastuty; Maghfirah, Fatwa Ridha; Rusli, Rusli; Rahman, Abdul
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4176

Abstract

The purpose of this study is to create multimedia-based teaching resources on quadrilaterals for eighth-grade students at SMP Negeri 1 Dua Boccoe. Research and development (R&D) is the methodology employed, and the ADDIE development model serves as the basis for the procedures. One math instructor, twenty-five eighth-grade students from SMP Negeri 1 Dua Boccoe, as well as media and content specialists, are the research subjects. In order to ascertain the practicality of the teaching materials in terms of learning quality, interest, and student independent learning, the following research instruments were used: (1) a material validation sheet by content experts, which aimed to assess the material content's suitability with the curriculum and learning objectives; (2) a material validation sheet by media experts, which aimed to assess the appearance, design, and feasibility of the multimedia used; and (3) a teacher response questionnaire. According to the study's findings, the instructional materials were deemed legitimate after being evaluated by media experts with a 95% approval rate and content experts with a 96% approval rate. Furthermore, based on the results of the teacher and student questionnaires, which covered ease, interest, and autonomous learning, the teaching materials were deemed practical with a 94% and 93% percentage, respectively. As a result, the multimedia-based teaching resources for mathematics for Class VIII pupils are deemed appropriate, fulfilling the requirements of being legitimate and useful for teaching the subject of quadrilaterals.
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.
A Public Sector Innovation: Determinants analysis of sustainability for Examining the Role of User Impact Mediation in the Dolan Banyumas Application Suci Lestari; Tobirin; Arif Muhamad Nurdin; Siti Balqis Huriyah
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4785

Abstract

This study examines the determinants of sustainability of public sector innovation by examining the mediating role of user innovation impact in the Dolan Banyumas application. This study uses a quantitative approach with the Structural Equation Modeling method based on Partial Least Squares (SEM-PLS). Data was collected through an online survey of 60 respondents who used the Dolan Banyumas application who were selected using the purposive sampling technique. The research model examines the relationship between three main constructs, namely the perception of innovation novelty, the impact of innovation, and the sustainability of innovation. The results of the study show that the perception of innovation novelty has a positive and significant effect on the impact of innovation and the sustainability of innovation. In addition, the impact of innovation has been shown to have a significant effect on the sustainability of innovation and effectively mediates the relationship between the perception of innovation novelty and innovation sustainability. The value of the determination coefficient shows that this research model is able to explain 73.9% variation in innovation impact and 73% variation in innovation sustainability. The main findings of this study show that the sustainability of digital innovation in the public sector is not only determined by the aspect of technological novelty alone, but is highly dependent on the benefits or real impacts that are directly felt by service users. Keywords: Public Sector Innovation, Digital Governance, Innovation Sustainability, Innovation Impact, SEM-PLS.
Application of LASSO Regression for the Identification of Underdeveloped Regions in Central Sulawesi Muh. Qodri Alfairus; Husnul Amira; Agung Tri Utomo; Nur Abshari Abbas
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4813

Abstract

This study aims to identify the main factors influencing regional underdevelopment in Central Sulawesi through Human Development Index (HDI) modeling and to develop a robust predictive model. To address the challenges of multicollinearity and the limited number of observations (13 districts/cities with 10 variables), this study employs LASSO (Least Absolute Shrinkage and Selection Operator) regression, which is capable of simultaneously shrinking coefficients and selecting variables. The data used are sourced from the 2019 publication of the Central Statistics Agency (BPS). The analysis was conducted using descriptive statistics, Ordinary Least Squares (OLS) modeling, VIF tests, and LASSO regression with cross-validation (leave-one-out cross-validation). The results indicate that very high multicollinearity (VIF > 10 for most variables) renders the OLS model unstable. Conversely, LASSO regression yielded better performance with superior RMSE (1.282), MAE (1.075), and R² (0.918) values compared to OLS (RMSE 21.67; MAE 9.85; R² 0.78). Thus, LASSO is more suitable for limited data with high multicollinearity. The selected significant variables include the percentage of the poor population, the open unemployment rate, shopping facilities, the presence of hospitals, the population density ratio, and the number of elementary and secondary schools.

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