Pub Date : 2018-12-30DOI: 10.14710/medstat.11.2.119-134
N. Hanifah, Fitri Kartiasih
The activity of textile sector and textile product (TPT) in Indonesia keeps growing from year to year.TPTIndustry has become the main contributor of foreign exchange from non-oil and gas sector. Unfortunately, the domestic supply of cotton fiber, main material of textile product, can’t fulfill textile industry’s demand. It forces the nation to import the raw materials. Based on the problem about the import that still exist until the present, it is necessary to do a research to analyze the development of cotton fiber import in Indonesia and to identify the factors affecting the development of Indonesian cotton fiber imports during 1975-2014. This research uses descriptive analysis and inference analysis. The descriptive analysis method used in this research is graphical analysis, while the inference analysis is Error Correction Mechanism (ECM) method. Based on the estimation made with ECM, it was found that 5 variables significantly affect the cotton import volume in the long term, including: real per capita Gross Domectic Product (GDP), international cotton fiber prices, domestic cotton fiber production, the demand of cotton fiber by domestic yarn spinning industry and textile product exports volume. While in short term, only 4 variables significantly affect thecotton fiber import volume: domestic cotton fiber production,the demand of cotton fiber by domestic yarn spinning industry, real per capita GDP and textile product exports volume. Keywords: import, cotton fiber, Textile Industry and Textile Product (TPT),Error Correction Mechanism (ECM).
{"title":"DETERMINAN IMPOR SERAT KAPAS DI INDONESIA TAHUN 1975-2014 (PENDEKATAN ERROR CORRECTION MECHANISM)","authors":"N. Hanifah, Fitri Kartiasih","doi":"10.14710/medstat.11.2.119-134","DOIUrl":"https://doi.org/10.14710/medstat.11.2.119-134","url":null,"abstract":"The activity of textile sector and textile product (TPT) in Indonesia keeps growing from year to year.TPTIndustry has become the main contributor of foreign exchange from non-oil and gas sector. Unfortunately, the domestic supply of cotton fiber, main material of textile product, can’t fulfill textile industry’s demand. It forces the nation to import the raw materials. Based on the problem about the import that still exist until the present, it is necessary to do a research to analyze the development of cotton fiber import in Indonesia and to identify the factors affecting the development of Indonesian cotton fiber imports during 1975-2014. This research uses descriptive analysis and inference analysis. The descriptive analysis method used in this research is graphical analysis, while the inference analysis is Error Correction Mechanism (ECM) method. Based on the estimation made with ECM, it was found that 5 variables significantly affect the cotton import volume in the long term, including: real per capita Gross Domectic Product (GDP), international cotton fiber prices, domestic cotton fiber production, the demand of cotton fiber by domestic yarn spinning industry and textile product exports volume. While in short term, only 4 variables significantly affect thecotton fiber import volume: domestic cotton fiber production,the demand of cotton fiber by domestic yarn spinning industry, real per capita GDP and textile product exports volume. Keywords: import, cotton fiber, Textile Industry and Textile Product (TPT),Error Correction Mechanism (ECM).","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/medstat.11.2.119-134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44953058","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 : 2018-12-30DOI: 10.14710/MEDSTAT.11.2.93-105
Wiwik Andriyani Lestari Ningsih, I. M. Arcana
Two aspects of efficiency that should be considered in applying sampling design of a survey are statistical efficiency and cost efficiency. Efficiency in statistical aspect improves precision of estimators obtained by the survey data, whereas efficiency in cost aspect provides an economic survey. The purpose of this researchis to evaluate the both efficiencies in all possible census blocks (CBs) sample setand to identify the best CBs sample set in the 2015 National Socio-Economic Survey (Susenas). Therefore, a computer program for calculating statistical, and cost efficiency aspects was developed in this research to determine the best sampel set of CBs among all possible sampel set of CBs based on sampling design of the 2015 Susenas implemented in Natuna District, Kepulauan Riau Province. The best possible sample set of CBs is determinedby considering statistical efficiency aspect, cost efficiency aspect, as well as combination of those two aspects. The result showed that the best sample set of CBs on statistical efficiency aspect provided the CBs sample set having minimum value of RSE index; evaluation on cost efficiency aspect provided the best CBs sample set having minimum value of total cost esimated using the total score of accessibility index; and evaluation on both efficiency aspects provided the best CBs sample set having minimum value of RSE index and minimum value of total score of accessibility index. Keywords : sampling design, all possible samples, statistical efficiency , cost efficienc y
{"title":"KAJIAN STATISTICAL DAN COST EFFICIENCY DALAM PENENTUAN GUGUS SAMPEL BLOK SENSUS TERBAIK (Studi Kasus: Sampling Design Susenas-2015 di Kabupaten Natuna)","authors":"Wiwik Andriyani Lestari Ningsih, I. M. Arcana","doi":"10.14710/MEDSTAT.11.2.93-105","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.2.93-105","url":null,"abstract":"Two aspects of efficiency that should be considered in applying sampling design of a survey are statistical efficiency and cost efficiency. Efficiency in statistical aspect improves precision of estimators obtained by the survey data, whereas efficiency in cost aspect provides an economic survey. The purpose of this researchis to evaluate the both efficiencies in all possible census blocks (CBs) sample setand to identify the best CBs sample set in the 2015 National Socio-Economic Survey (Susenas). Therefore, a computer program for calculating statistical, and cost efficiency aspects was developed in this research to determine the best sampel set of CBs among all possible sampel set of CBs based on sampling design of the 2015 Susenas implemented in Natuna District, Kepulauan Riau Province. The best possible sample set of CBs is determinedby considering statistical efficiency aspect, cost efficiency aspect, as well as combination of those two aspects. The result showed that the best sample set of CBs on statistical efficiency aspect provided the CBs sample set having minimum value of RSE index; evaluation on cost efficiency aspect provided the best CBs sample set having minimum value of total cost esimated using the total score of accessibility index; and evaluation on both efficiency aspects provided the best CBs sample set having minimum value of RSE index and minimum value of total score of accessibility index. Keywords : sampling design, all possible samples, statistical efficiency , cost efficienc y","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.2.93-105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49544178","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 : 2018-12-30DOI: 10.14710/MEDSTAT.11.2.147-158
Ihdayani Banun Afa, S. Suparti, Rita Rahmawati
The composite stock price index or Indonesia Composite Index (ICI) is a composite index of all stocks listed on the Indonesia Stock Exchange and its movements indicate conditions that occur in the capital market. For investors, the ICI movement is one of the important indicator to make a decision whether the stocks will be sold, held or bought new shares. The ICI movement (y) was influenced by several factors including Inflation (x 1 ), Exchange Rate (x 2 ) and SBI interest rate (x 3 ). This study aims to compare the ICI modeling using the parameric and nonparametric approaches, namely multivariable linear regression and multivariable spline regression. Determination of the better model is based on the smaller MSE and the larger R 2 . The best regression model is multivariable spline regression with x 1 , x 2 and x 3 , each with a sequence orde (3,2,2) and the number of knot points (1,2,2). Keywords: Indonesia Composite Index, Multiple Linear Regression, Multivariable Spline Regression, MSE, R 2
{"title":"PERBANDINGAN METODE REGRESI LINIER MULTIVARIABEL DAN REGRESI SPLINE MULTIVARIABEL DALAM PEMODELAN INDEKS HARGA SAHAM GABUNGAN","authors":"Ihdayani Banun Afa, S. Suparti, Rita Rahmawati","doi":"10.14710/MEDSTAT.11.2.147-158","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.2.147-158","url":null,"abstract":"The composite stock price index or Indonesia Composite Index (ICI) is a composite index of all stocks listed on the Indonesia Stock Exchange and its movements indicate conditions that occur in the capital market. For investors, the ICI movement is one of the important indicator to make a decision whether the stocks will be sold, held or bought new shares. The ICI movement (y) was influenced by several factors including Inflation (x 1 ), Exchange Rate (x 2 ) and SBI interest rate (x 3 ). This study aims to compare the ICI modeling using the parameric and nonparametric approaches, namely multivariable linear regression and multivariable spline regression. Determination of the better model is based on the smaller MSE and the larger R 2 . The best regression model is multivariable spline regression with x 1 , x 2 and x 3 , each with a sequence orde (3,2,2) and the number of knot points (1,2,2). Keywords: Indonesia Composite Index, Multiple Linear Regression, Multivariable Spline Regression, MSE, R 2","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.2.147-158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43010799","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 : 2018-12-30DOI: 10.14710/MEDSTAT.11.2.79-91
Arya Fendha Ibnu Shina
Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations
{"title":"ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB","authors":"Arya Fendha Ibnu Shina","doi":"10.14710/MEDSTAT.11.2.79-91","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.2.79-91","url":null,"abstract":"Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.2.79-91","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49270107","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 : 2018-12-30DOI: 10.14710/MEDSTAT.11.2.135-145
A. Prahutama, B. Warsito, M. Mukid
Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression. Keywords: Maternal and Infant mortality, Poisson, GWPR
{"title":"ANALYSIS OF THE NUMBER INFANT AND MATERNAL MORTALITY IN CENTRAL JAVA INDONESIA USING SPATIAL-POISSON REGRESSION","authors":"A. Prahutama, B. Warsito, M. Mukid","doi":"10.14710/MEDSTAT.11.2.135-145","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.2.135-145","url":null,"abstract":"Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression. Keywords: Maternal and Infant mortality, Poisson, GWPR","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.2.135-145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43305474","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 : 2018-09-29DOI: 10.14710/MEDSTAT.11.1.65-78
Tarno Tarno, Agus Rusgiyono, Budi Warsito, S. Sudarno, Dwi Ispriyanti
The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually. Keywords: Time Series , Potato produ ction , hybrid, ANFIS, ARIMA, LM-test
{"title":"PEMODELAN HYBRID ARIMA-ANFIS UNTUK DATA PRODUKSI TANAMAN HORTIKULTURA DI JAWA TENGAH","authors":"Tarno Tarno, Agus Rusgiyono, Budi Warsito, S. Sudarno, Dwi Ispriyanti","doi":"10.14710/MEDSTAT.11.1.65-78","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.1.65-78","url":null,"abstract":"The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually. Keywords: Time Series , Potato produ ction , hybrid, ANFIS, ARIMA, LM-test","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.1.65-78","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45528520","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 : 2018-09-29DOI: 10.14710/MEDSTAT.11.1.1-15
Yosephine Magdalena Sitorus, Lia Yuliana
There is inequality between the economic growth of provinces in Java and outside of Java. The total area of Java is only 6,77% from total area of Indonesia but the Growth Domestic Product (GDP) based on constant price in 2014, Java contributed 57,8% of the GDP total Indonesia. One cause that made this disparity is the development of infrastructure in outside Java is still weak. The development of infrastructure is a basic element for increasing total output production that later will increase the economic growth. However, there are so many problems that occur in developing the infrastructure in outside of Java. This study aimed to analyze the condition of infrastructure provinces outside Java in 2010-2014. The data used is the secondary data for 27 provinces outside of Java 2010-2014 from BPS. The analytical method used is panel data regression with fixed effect model and Seemingly Unrelated Regression (SUR) Model. Based on the results, the infrastructure that affects economic productivity significantly and positively is road infrastructure, health, and budget. Infrastructure that affects economic productivity significantly and negatively is the educational infrastructure. Water and electricity infrastructure did not significantly affect economic productivity.Keywords: Infrastructure, Economic productivity, Panel Data Regression, Fixed Effect Model
{"title":"PENERAPAN REGRESI DATA PANEL PADA ANALISIS PENGARUH INFRASTRUKTUR TERHADAP PRODUKTIFITAS EKONOMI PROVINSI-PROVINSI DI LUAR PULAU JAWA TAHUN 2010-2014","authors":"Yosephine Magdalena Sitorus, Lia Yuliana","doi":"10.14710/MEDSTAT.11.1.1-15","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.1.1-15","url":null,"abstract":"There is inequality between the economic growth of provinces in Java and outside of Java. The total area of Java is only 6,77% from total area of Indonesia but the Growth Domestic Product (GDP) based on constant price in 2014, Java contributed 57,8% of the GDP total Indonesia. One cause that made this disparity is the development of infrastructure in outside Java is still weak. The development of infrastructure is a basic element for increasing total output production that later will increase the economic growth. However, there are so many problems that occur in developing the infrastructure in outside of Java. This study aimed to analyze the condition of infrastructure provinces outside Java in 2010-2014. The data used is the secondary data for 27 provinces outside of Java 2010-2014 from BPS. The analytical method used is panel data regression with fixed effect model and Seemingly Unrelated Regression (SUR) Model. Based on the results, the infrastructure that affects economic productivity significantly and positively is road infrastructure, health, and budget. Infrastructure that affects economic productivity significantly and negatively is the educational infrastructure. Water and electricity infrastructure did not significantly affect economic productivity.Keywords: Infrastructure, Economic productivity, Panel Data Regression, Fixed Effect Model","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.1.1-15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45902764","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 : 2018-09-29DOI: 10.14710/MEDSTAT.11.1.53-64
Hasbi Yasin, Budi Warsito, Arief Rachman Hakim
Economic growth can be measured by amount of Gross Regional Domestic Product (GRDP). Based on official news of statistics BPS, Economic growth in Banten region has increase up to 5.59%. It supported by several sector, there are agriculture, business, industry and from various fields. Mixed Geographically Weighted Regression (MGWR) methods have been developed based on linear regression by giving spatial effect or location (longitude and latitude), the resulting model from Economic growth in Banten will be local or different based on each location. MGWR mixed method between linear regression and GWR, parameters in linear regression are global and GWR parameters are local. The results more specific because economic growth in Banten region assessed by location.Keywords: Banten, Economic growth, MGWR.
{"title":"PEMODELAN PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION","authors":"Hasbi Yasin, Budi Warsito, Arief Rachman Hakim","doi":"10.14710/MEDSTAT.11.1.53-64","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.1.53-64","url":null,"abstract":"Economic growth can be measured by amount of Gross Regional Domestic Product (GRDP). Based on official news of statistics BPS, Economic growth in Banten region has increase up to 5.59%. It supported by several sector, there are agriculture, business, industry and from various fields. Mixed Geographically Weighted Regression (MGWR) methods have been developed based on linear regression by giving spatial effect or location (longitude and latitude), the resulting model from Economic growth in Banten will be local or different based on each location. MGWR mixed method between linear regression and GWR, parameters in linear regression are global and GWR parameters are local. The results more specific because economic growth in Banten region assessed by location.Keywords: Banten, Economic growth, MGWR.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.1.53-64","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48259551","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 : 2018-09-29DOI: 10.14710/MEDSTAT.11.1.27-38
Untung Kurniawan
Bivariate Poisson models are appropriate for modeling paired count data exhibiting correlation. This study aims to estimates the parameters and test hypothesis of bivariate Poisson regression on modeling the number of infant mortality and maternal mortality in Central Java 2015. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Results show that the percentage of births by health personnel, the percentage of pregnant women administered the K4 program, the percentage of pregnant women receiving Fe3 tablets, percentage of exclusively breastfed infants, and percentage of households behaved in a clean and healthy life are significant for the number of infant mortality in Central Java. The variables that have significant effect on maternal mortality are percentage of births by health personnel, percentage of maternal women receiving postpartum health services, and percentage of pregnant women receiving Fe3 tablets. Keywords: Bivariate Poisson Regression, Infant Mortality, Maternal Mortality, Maximum Likelihood Estimation
{"title":"MODEL REGRESI POISON BIVARIAT DENGAN KOVARIAN KONSTAN","authors":"Untung Kurniawan","doi":"10.14710/MEDSTAT.11.1.27-38","DOIUrl":"https://doi.org/10.14710/MEDSTAT.11.1.27-38","url":null,"abstract":"Bivariate Poisson models are appropriate for modeling paired count data exhibiting correlation. This study aims to estimates the parameters and test hypothesis of bivariate Poisson regression on modeling the number of infant mortality and maternal mortality in Central Java 2015. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Results show that the percentage of births by health personnel, the percentage of pregnant women administered the K4 program, the percentage of pregnant women receiving Fe3 tablets, percentage of exclusively breastfed infants, and percentage of households behaved in a clean and healthy life are significant for the number of infant mortality in Central Java. The variables that have significant effect on maternal mortality are percentage of births by health personnel, percentage of maternal women receiving postpartum health services, and percentage of pregnant women receiving Fe3 tablets. Keywords: Bivariate Poisson Regression, Infant Mortality, Maternal Mortality, Maximum Likelihood Estimation","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.11.1.27-38","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46519922","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 : 2018-09-29DOI: 10.14710/MEDSTAT.11.1.17-26
Saiful Ghozi, Ramli Ramli, Asri Setyani
This paper analyze factors that influence customer preference between conventional and sharia bank, and which factor is the most dominant. The study was conducted in Balikpapan city from May 2017 until August 2017. The sample is 25 customers of BRI Sharia and 31 customers of conventional BRI. Statistical analysis model used in this paper is Binary Logistics Regression. There are 8 predictor variables to be analyzed to know their effect to customer decision in choosing bank between sharia bank and conventional bank. The variables are: knowledge of respondents about sharia bank (X1), knowledge of respondents about the difference between conventional and sharia banks (X2), knowledge of respondents about products offered by sharia bank (X3), promotion of sharia bank via printed media (X4), promotion of sharia bank via electronic media (X5), promotion of sharia bank in social activities (X6), the customer's efforts to observe religious orders (X7), and the customer's efforts to avoid the religious prohibitions (X8). The results of individual significance test indicate that knowledge of respondents about sharia bank, and promotion of sharia bank through electronic media has significant effect to the customer’s decision in choosing bank. And the most significant effect is promotion through electronic media (X5). Keywords : binary logistic regression, decision, sharia bank
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