Pub Date : 2023-05-26DOI: 10.35877/mathscience1734
Irwan Irwan, M. Abdy, Nurul Khofifah Salsabila, A. Ahmar
The purpose of this study was to determine the optimal portfolio in the telecommunications sector listed on the Indonesia Stock Exchange based on the Jakarta Composite Index for the period January 2018–December 2020 using the Single Index Model. This type of research is an applied research. This type of research is applied research with secondary data obtained from the Indonesia Stock Exchange, Yahoo Finance, and Bank Indonesia. The number of samples taken is 5 stocks, namely TLKM, ISAT, EXCL, BTEL, and FREN. Based on the results of the analysis of the 5 stocks that are members of the JCI, the combination of 2 stocks that make up the optimal portfolio, namely ISAT and FREN, produces an expected return of 5.08% with a risk of 8.02%.
{"title":"Analysis of Stock Portfolio Optimization in the Telecommunications Sector Using the Single Index Model","authors":"Irwan Irwan, M. Abdy, Nurul Khofifah Salsabila, A. Ahmar","doi":"10.35877/mathscience1734","DOIUrl":"https://doi.org/10.35877/mathscience1734","url":null,"abstract":"The purpose of this study was to determine the optimal portfolio in the telecommunications sector listed on the Indonesia Stock Exchange based on the Jakarta Composite Index for the period January 2018–December 2020 using the Single Index Model. This type of research is an applied research. This type of research is applied research with secondary data obtained from the Indonesia Stock Exchange, Yahoo Finance, and Bank Indonesia. The number of samples taken is 5 stocks, namely TLKM, ISAT, EXCL, BTEL, and FREN. Based on the results of the analysis of the 5 stocks that are members of the JCI, the combination of 2 stocks that make up the optimal portfolio, namely ISAT and FREN, produces an expected return of 5.08% with a risk of 8.02%.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.35877/mathscience1768
Nadya Maharani Vega, Annisa Anugrah Damaiyanti, A. S. M. Arhamar, Agung Wijaya Ami, Saparuddin Saparuddin
One of the causes of the decline in the quality of education is the large number of students who experience learning difficulties. The purpose of this study was to determine the learning difficulties experienced by students in class XI MAN 2 Makassar. The aspects observed in learning difficulties at MAN 2 Makassar include learning motivation, learning media, learning processes, use of learning tools, as well as learning support facilities and facilities. This study uses a quantitative approach using a survey method research type. The population in this study were students from class XI MIPA at MAN 2 Makassar and the sample in this study were students from MAN 2 Makassar class XI MIPA 1 and MIPA 2 with a total of 53 participants. The data collection technique uses a survey which contains 30 questions developed based on aspects of learning difficulties and distributed via the Google form. The results showed that the learning difficulties of students in class XI MIPA at MAN 2 Makassar in the aspect of learning motivation had a moderate categorization with an index of 55.07%, low learning media with an index of 79.4%, a low learning process with an index of 74%, the use of learning tools low with an index of 66.7% and learning support facilities and facilities which are classified as low with an index of 66.1%.
{"title":"Analysis of Learning Difficulties for Students of MAN 2 Makassar","authors":"Nadya Maharani Vega, Annisa Anugrah Damaiyanti, A. S. M. Arhamar, Agung Wijaya Ami, Saparuddin Saparuddin","doi":"10.35877/mathscience1768","DOIUrl":"https://doi.org/10.35877/mathscience1768","url":null,"abstract":"One of the causes of the decline in the quality of education is the large number of students who experience learning difficulties. The purpose of this study was to determine the learning difficulties experienced by students in class XI MAN 2 Makassar. The aspects observed in learning difficulties at MAN 2 Makassar include learning motivation, learning media, learning processes, use of learning tools, as well as learning support facilities and facilities. This study uses a quantitative approach using a survey method research type. The population in this study were students from class XI MIPA at MAN 2 Makassar and the sample in this study were students from MAN 2 Makassar class XI MIPA 1 and MIPA 2 with a total of 53 participants. The data collection technique uses a survey which contains 30 questions developed based on aspects of learning difficulties and distributed via the Google form. The results showed that the learning difficulties of students in class XI MIPA at MAN 2 Makassar in the aspect of learning motivation had a moderate categorization with an index of 55.07%, low learning media with an index of 79.4%, a low learning process with an index of 74%, the use of learning tools low with an index of 66.7% and learning support facilities and facilities which are classified as low with an index of 66.1%.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134378934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.35877/mathscience1763
Muhammad Refaldy, S. Annas, Z. Rais
Clustering is something that is used to analyze data in machine learning, data mining, pattern engineering, image analysis, and bioinformatics. To produce the information needed for a data analysis using the clustering process, this is because the data has a large variety and amount. Researchers will use the K-Prototype method where this method becomes an efficient and effective algorithm in processing mixed-type data. The K-Prototype algorithm has problems in finding the best number of clusters. So, in this paper, researchers will conduct research by finding the best number of clusters in the K-Prototype method. There are many ways to determine this, one of which is the Elbow method. The determination of this method is seen from the SSE (Sum Square Error) graph of several number of clusters. The results of the clustering formed 2 clusters which were considered optimal based on the value of k that experienced the greatest decrease. The results showed that Cluster 1 is a cluster that has characteristics of people's welfare which is better than Cluster 2
{"title":"K-Prototype Algorithm in Grouping Regency/City in South Sulawesi Province Based on 2020 People's Welfare","authors":"Muhammad Refaldy, S. Annas, Z. Rais","doi":"10.35877/mathscience1763","DOIUrl":"https://doi.org/10.35877/mathscience1763","url":null,"abstract":"Clustering is something that is used to analyze data in machine learning, data mining, pattern engineering, image analysis, and bioinformatics. To produce the information needed for a data analysis using the clustering process, this is because the data has a large variety and amount. Researchers will use the K-Prototype method where this method becomes an efficient and effective algorithm in processing mixed-type data. The K-Prototype algorithm has problems in finding the best number of clusters. So, in this paper, researchers will conduct research by finding the best number of clusters in the K-Prototype method. There are many ways to determine this, one of which is the Elbow method. The determination of this method is seen from the SSE (Sum Square Error) graph of several number of clusters. The results of the clustering formed 2 clusters which were considered optimal based on the value of k that experienced the greatest decrease. The results showed that Cluster 1 is a cluster that has characteristics of people's welfare which is better than Cluster 2","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"194 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132571903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.35877/mathscience1740
Sutamrin, Khadijah, Isma Muthahharah
This study aims to determine the distribution of origins of prospective new STKIP Pembangunan Indonesia students during the COVID-19 pandemic based on the student's name, religion adopted, chosen study program, and sources of information on new student admissions. The method used is clustering with a total sample of 27 regions in Indonesia. In this study, 3 clusters were formed, namely cluster 1 which had the most students with members namely East Jakarta, East Kalimantan, Gowa, Maros, Takalar, Bantaeng, Manggarai, West Manggarai, East Flores, West Sumba. Cluster 2 has not too many (moderate) prospective students with members namely Makassar, Barru, SInjai, Bulukumba, Soppeng, Enrekang, Jeneponto, Selayar, Polewali Mandar, East Maggarai. Cluster 3 has the fewest prospective students with members namely Makassar, Barru, Sinjai, Bulukumba, Soppeng, Enrekang, Jeneponto, Selayar, Polewali Mandar, East Maggarai
{"title":"Cluster Analysis of New Students at STKIP Pembangunan Indonesia during the COVID-19 Pandemic Based on Regional Origin","authors":"Sutamrin, Khadijah, Isma Muthahharah","doi":"10.35877/mathscience1740","DOIUrl":"https://doi.org/10.35877/mathscience1740","url":null,"abstract":"This study aims to determine the distribution of origins of prospective new STKIP Pembangunan Indonesia students during the COVID-19 pandemic based on the student's name, religion adopted, chosen study program, and sources of information on new student admissions. The method used is clustering with a total sample of 27 regions in Indonesia. In this study, 3 clusters were formed, namely cluster 1 which had the most students with members namely East Jakarta, East Kalimantan, Gowa, Maros, Takalar, Bantaeng, Manggarai, West Manggarai, East Flores, West Sumba. Cluster 2 has not too many (moderate) prospective students with members namely Makassar, Barru, SInjai, Bulukumba, Soppeng, Enrekang, Jeneponto, Selayar, Polewali Mandar, East Maggarai. Cluster 3 has the fewest prospective students with members namely Makassar, Barru, Sinjai, Bulukumba, Soppeng, Enrekang, Jeneponto, Selayar, Polewali Mandar, East Maggarai","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115426791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.35877/mathscience1761
Taufiq Hidayat, R. Ruliana, Z. Rais, M. Botto-Tobar
Cluster analysis is a data mining technique used to group data based on the similarity of attributes of object data. One of the problems that are often encountered in cluster analysis is data with a mixed categorical and numerical scale. The clustering stage for mixed data using the ensemble ROCK (Robust Clustering using links) method is carried out by combining clustering outputs from categorical and numeric scale data. The method used for categorical data is the ROCK method and the method used for numerical data is the Hierarchical Agglomerative method. The best clustering method is determined based on the criteria for the ratio between the standard deviations within the group (SW) and the smallest standard deviation between groups (SB). Based on 24 observation objects in the regencies and cities of the Province of South Sulawesi, the ROCK ensemble method with a value of 0.1 produces three clusters with a ratio value of 2,27 x10-16 based on the combination of the output results of the ROCK method and the Hierarchical Agglomerative method
{"title":"Cluster Analysis Using Ensemble ROCK Method in District/City Grouping in South Sulawesi Province based on People's Welfare Indicators","authors":"Taufiq Hidayat, R. Ruliana, Z. Rais, M. Botto-Tobar","doi":"10.35877/mathscience1761","DOIUrl":"https://doi.org/10.35877/mathscience1761","url":null,"abstract":"Cluster analysis is a data mining technique used to group data based on the similarity of attributes of object data. One of the problems that are often encountered in cluster analysis is data with a mixed categorical and numerical scale. The clustering stage for mixed data using the ensemble ROCK (Robust Clustering using links) method is carried out by combining clustering outputs from categorical and numeric scale data. The method used for categorical data is the ROCK method and the method used for numerical data is the Hierarchical Agglomerative method. The best clustering method is determined based on the criteria for the ratio between the standard deviations within the group (SW) and the smallest standard deviation between groups (SB). Based on 24 observation objects in the regencies and cities of the Province of South Sulawesi, the ROCK ensemble method with a value of 0.1 produces three clusters with a ratio value of 2,27 x10-16 based on the combination of the output results of the ROCK method and the Hierarchical Agglomerative method","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121520503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Improved Exponential Approach Method in Determining Optimum Solutions for Transportation Problems","authors":"Rusli, Sukarna, Wahyudin","doi":"10.35877/mathscience744","DOIUrl":"https://doi.org/10.35877/mathscience744","url":null,"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.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133571040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Numerical Solution of the Mathematical Model of DHF Spread using the Runge-Kutta Fourth Order Method","authors":"S. Side, A. Zaki, Miswar","doi":"10.35877/mathscience745","DOIUrl":"https://doi.org/10.35877/mathscience745","url":null,"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.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122150599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua","authors":"Fauziah, M. Tiro, Ruliana","doi":"10.35877/mathscience741","DOIUrl":"https://doi.org/10.35877/mathscience741","url":null,"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.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132309439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"K-Means Cluster Analysis for Grouping Districts in South Sulawesi Province Based on Village Potential","authors":"Azrahwati, M. Nusrang, M. Aidid, Z. Rais","doi":"10.35877/mathscience739","DOIUrl":"https://doi.org/10.35877/mathscience739","url":null,"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.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129065397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province","authors":"Irma Yani Safitri, M. Tiro, Ruliana","doi":"10.35877/mathscience740","DOIUrl":"https://doi.org/10.35877/mathscience740","url":null,"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.","PeriodicalId":431947,"journal":{"name":"ARRUS Journal of Mathematics and Applied Science","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131088087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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