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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 10 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2022)" : 10 Documents clear
K-Means Cluster Analysis for Grouping Districts in South Sulawesi Province Based on Village Potential Azrahwati; Nusrang, Muhammad; Aidid, Muhammad Kasim; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience739

Abstract

Cluster analysis is an analysis in multivariable statistics that is used to group objects that have the same characteristics. One of the methods in cluster analysis used to group relatively large amounts of data is the K-Means method. In this study, the K-Means method was applied to classify sub-districts in South Sulawesi Province based on village potential. The variables used are the number of: Elementary School/Equivalent degree, Junior High School/Equivalent degree, Senior High School/Vocational School/Equivalent degree, Community Health Center/Pustu, Families without electricity, Villages/Urbans according to market presence, Villages/Towns that are passed by public transportation and Villages/Kelurahan that have lighting main road. The results of this study are that 3 groups are formed where the first cluster consists of 107 sub-districts that have high village potential, the second cluster consists of 16 sub-districts that have medium village potential and the third cluster consists of 184 sub-districts that have low village potential.
Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province Safitri, Irma Yani; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience740

Abstract

Spatial regression is a development of classical linear regression which is based on the influence of place or location. To determine the location/spatial effect, a spatial dependency test was performed using the Moran Index, and the Lagrange Multiplier (LM) test was used to determine a significant spatial regression model. In this study, spatial regression was applied to the case of food security in each district in South Sulawesi Province. The results of the analysis show that there is a negative spatial autocorrelation, meaning that the spatial effect does not affect the level of food security. The significant spatial regression model is the SEM (Spatial Error Model) model. The equation of the SEM model produces variables that have a significant effect, namely the ratio of normative consumption per capita to net availability, percentage of population living below the poverty line, percentage of households with a proportion of expenditure on food more than 65 percent of total expenditure, percentage of households without access to electricity, percentage of households without access to clean water, life expectancy at birth, ratio of population per health worker to the level of population density, the average length of schooling for women above 15 years, and the percentage of children under five with height below standard (stunting). Thus, the resulting distribution pattern is a uniform data pattern. This means that each adjacent district tends to have different characteristics.
Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua Fauziah; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience741

Abstract

Classification is a job of assessing data objects to include them in a particular class from a number of available classes. The classification method used is the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods. The data used in this study is data on poverty in Papua with the category of the number of low/high level poor people. Of the 29 regencies/cities that were sampled, 15 regencies/cities represent the number of low-level poor people and 14 districts/cities are the number of high-level poor people. The results of the analysis obtained are the k-Nearest Neighbor (k-NN) method with a value of k=15 producing an accuracy of 58.62%, while the Support Vector Machine (SVM) method with Parameter cost = 1 using the RBF kernel produces an accuracy value. by 93.1%. The classification criteria to find the best method is to look at the Root Mean Square Error (RMSE) which states that the Support Vector Machine (SVM) method is better than the k-Nearest Neighbor (k-NN) method.
Improved Exponential Approach Method in Determining Optimum Solutions for Transportation Problems Rusli; Sukarna; Wahyudin
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience744

Abstract

This study describes the transportation methods that regulate and distribute resources that provide products where they are needed to achieve efficient transportation costs. Solve a transportation problem in this thesis using the Improved Exponential Approach method, then using the NWC (Northwest) method to test its optimization. The purpose of this research is to get more optimal results as initial consideration to increase the distribution cost savings in the Bread Company. Costs incurred by the company before the study amounted to Rp.3,218,000. The results of this study found that the application of the transportation method using the Improved Exponential Approach method is effectively used compared to the NWC method which has a comparison of transportation costs of Rp. 2,612,500 and Rp. 2,785,000, Optimization test results obtained from the Improved Exponential Approach method amounted to Rp2,612,500. And the Improved Exponential Approach method used by researchers can be applied to the Gardenia company.
Numerical Solution of the Mathematical Model of DHF Spread using the Runge-Kutta Fourth Order Method Side, Syafruddin; Zaki, Ahmad; Miswar
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience745

Abstract

This research was conducted to find a numerical solution to the mathematical model of DHF in Makassar using the Runge-Kutta fourth order method. The mathematical model of DHF is in the form of a system of differential equations that includes variables S (Susceptible), E (Exposed), I (Infected), and R (Recovery) simplified into classes of vulnerable (S), exposed (E), infected (I) and cured (R) as initial value. Parameters value that is solved numerically using the Runge-Kutta fourth order method with time intervals h = 0.01 months using data from South Sulawesi Provincial Health Service in 2017. Based on the initial value of each class, namely: obtained (Sh1) =10910.4, (E) = 0, (Ih1) = 177.9 , (Sv1) = 5018685.6, (Iv1) = 135.4, and R = -981612.3. The initial values ​​and parameter values ​​are substituted into numerical solutions to the model simulated using maple as a tool.
K-Means Cluster Analysis for Grouping Districts in South Sulawesi Province Based on Village Potential Azrahwati; Nusrang, Muhammad; Aidid, Muhammad Kasim; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience739

Abstract

Cluster analysis is an analysis in multivariable statistics that is used to group objects that have the same characteristics. One of the methods in cluster analysis used to group relatively large amounts of data is the K-Means method. In this study, the K-Means method was applied to classify sub-districts in South Sulawesi Province based on village potential. The variables used are the number of: Elementary School/Equivalent degree, Junior High School/Equivalent degree, Senior High School/Vocational School/Equivalent degree, Community Health Center/Pustu, Families without electricity, Villages/Urbans according to market presence, Villages/Towns that are passed by public transportation and Villages/Kelurahan that have lighting main road. The results of this study are that 3 groups are formed where the first cluster consists of 107 sub-districts that have high village potential, the second cluster consists of 16 sub-districts that have medium village potential and the third cluster consists of 184 sub-districts that have low village potential.
Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province Safitri, Irma Yani; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience740

Abstract

Spatial regression is a development of classical linear regression which is based on the influence of place or location. To determine the location/spatial effect, a spatial dependency test was performed using the Moran Index, and the Lagrange Multiplier (LM) test was used to determine a significant spatial regression model. In this study, spatial regression was applied to the case of food security in each district in South Sulawesi Province. The results of the analysis show that there is a negative spatial autocorrelation, meaning that the spatial effect does not affect the level of food security. The significant spatial regression model is the SEM (Spatial Error Model) model. The equation of the SEM model produces variables that have a significant effect, namely the ratio of normative consumption per capita to net availability, percentage of population living below the poverty line, percentage of households with a proportion of expenditure on food more than 65 percent of total expenditure, percentage of households without access to electricity, percentage of households without access to clean water, life expectancy at birth, ratio of population per health worker to the level of population density, the average length of schooling for women above 15 years, and the percentage of children under five with height below standard (stunting). Thus, the resulting distribution pattern is a uniform data pattern. This means that each adjacent district tends to have different characteristics.
Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua Fauziah; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience741

Abstract

Classification is a job of assessing data objects to include them in a particular class from a number of available classes. The classification method used is the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods. The data used in this study is data on poverty in Papua with the category of the number of low/high level poor people. Of the 29 regencies/cities that were sampled, 15 regencies/cities represent the number of low-level poor people and 14 districts/cities are the number of high-level poor people. The results of the analysis obtained are the k-Nearest Neighbor (k-NN) method with a value of k=15 producing an accuracy of 58.62%, while the Support Vector Machine (SVM) method with Parameter cost = 1 using the RBF kernel produces an accuracy value. by 93.1%. The classification criteria to find the best method is to look at the Root Mean Square Error (RMSE) which states that the Support Vector Machine (SVM) method is better than the k-Nearest Neighbor (k-NN) method.
Improved Exponential Approach Method in Determining Optimum Solutions for Transportation Problems Rusli, Rusli; Sukarna, Sukarna; Wahyudin, Wahyudin
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience744

Abstract

This study describes the transportation methods that regulate and distribute resources that provide products where they are needed to achieve efficient transportation costs. Solve a transportation problem in this thesis using the Improved Exponential Approach method, then using the NWC (Northwest) method to test its optimization. The purpose of this research is to get more optimal results as initial consideration to increase the distribution cost savings in the Bread Company. Costs incurred by the company before the study amounted to Rp.3,218,000. The results of this study found that the application of the transportation method using the Improved Exponential Approach method is effectively used compared to the NWC method which has a comparison of transportation costs of Rp. 2,612,500 and Rp. 2,785,000, Optimization test results obtained from the Improved Exponential Approach method amounted to Rp2,612,500. And the Improved Exponential Approach method used by researchers can be applied to the Gardenia company.
Numerical Solution of the Mathematical Model of DHF Spread using the Runge-Kutta Fourth Order Method Side, Syafruddin; Zaki, Ahmad; Miswar
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience745

Abstract

This research was conducted to find a numerical solution to the mathematical model of DHF in Makassar using the Runge-Kutta fourth order method. The mathematical model of DHF is in the form of a system of differential equations that includes variables S (Susceptible), E (Exposed), I (Infected), and R (Recovery) simplified into classes of vulnerable (S), exposed (E), infected (I) and cured (R) as initial value. Parameters value that is solved numerically using the Runge-Kutta fourth order method with time intervals h = 0.01 months using data from South Sulawesi Provincial Health Service in 2017. Based on the initial value of each class, namely: obtained (Sh1) =10910.4, (E) = 0, (Ih1) = 177.9 , (Sv1) = 5018685.6, (Iv1) = 135.4, and R = -981612.3. The initial values ​​and parameter values ​​are substituted into numerical solutions to the model simulated using maple as a tool.

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