{"title":"Spatial Autoregressive Model untuk Pemodelan Angka Harapan Hidup (AHH) di Provinsi Jawa Timur","authors":"Nova Ratih Intan, Edy Sulistiyawan","doi":"10.36456/jstat.vol11.no2.a2178","DOIUrl":"https://doi.org/10.36456/jstat.vol11.no2.a2178","url":null,"abstract":"Life expectancy is an estimation of life spans that can be attained in a region. Life expectancy is an indicator of the amount of a country’s public health. Life expectancy also can be a benchmark for evaluating the government’s performance in health, social and economic fields. So, we need a statistic model to analyze the factors that affect life expectancy in East Java. The data analysis using multiple linear regression method with Ordinary Least Square (OLS) approach is not enough if some of OLS assumption is not fulfilled. That is why to overcome that problem we use Spatial Autoregressive Model (SAR) method which is used to know the spatial lag on variable response and parameter estimate. According to the data analysis, on the spatial aspect the data has fulfilled the assumption of spatial dependency using Moran’s I with p-value of 0,004315. The spatial weighted matrices that is used is weighted matrices Queen Contiguity. There is the coefficient of determination value (R2) and Akaike’s Information Criterion (AIC) from Spatial Autoregressive Model that is better than OLS consecutively that is 72,459% and 137,36. The significant factor that affect life expectancy on every region/city in East Java is the percentage of households that live clean and health (X7) and the percentage of poor people (X8). \u0000 \u0000Angka Harapan Hidup adalah perkiraan usia hidup yang dapat dicapai oleh penduduk pada suatu wilayah. Angka harapan hidup digunakan sebagai salah satu indikator derajat kesehatan masyarakat suatu negara. Angka harapan hidup juga dapat menjadi tolak ukur untuk mengevaluasi kinerja pemerintah dalam bidang kesehatan, sosial dan ekonomi. Oleh karena itu diperlukan sebuah pemodelan statistika untuk menganalisis faktor-faktor yang mempengaruhi angka harapan hidup di Jawa Timur. Analisis data menggunakan metode regresi linear berganda dengan pendekatan Ordinary Least Square (OLS) tidak cukup jika beberapa asumsi OLS tidak terpenuhi. Maka untuk mengatasi hal tersebut digunakan metode Spatial Autoregressive Model (SAR) yang digunakan untuk mengetahui lag spasial pada variabel respon dan menaksir parameter. Berdasarkan hasil analisis, pada aspek spasial data telah memenuhi asumsi dependensi spasial menggunakan uji Moran’s I dengan p-value sebesar 0,004315. Matriks pembobot yang digunakan adalah matriks pembobot Queen Contiguity. Diperoleh nilai koefisien determinasi (R2) dan Akaike’s Information Criterion (AIC) dari Spatial Autoregressive Model yang lebih baik dari OLS berturut-turut yaitu 72,459% dan 137,36. Faktor signifikan yang mempengaruhi AHH di setiap kabupaten/kota di Jawa Timur adalah persentase rumah tangga berperilaku hidup bersih dan sehat (X7) dan persentase penduduk miskin (X8).","PeriodicalId":118320,"journal":{"name":"J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128978268","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}
{"title":"Pemodelan Kemiskinan Pada Kabupaten/Kota Di Provinsi Jawa Timur Tahun 2015 Dengan Pendekatan Model Regresi Spasial","authors":"Siti Munadhiroh, Wara Pramesti","doi":"10.36456/jstat.vol11.no2.a1864","DOIUrl":"https://doi.org/10.36456/jstat.vol11.no2.a1864","url":null,"abstract":"Regression is a technique that can be used for response variables with one or more predictor variables. The purpose of this study is to model poverty in districts / cities in East Java 2015 with a spatial regression approach. In 2015, poverty in East Java has increased compared to the previous year. Therefore it is necessary to identify the factors that affect poverty. The variables used are the percentage of poor population as the response variable and the predictor variables include last elementary school education (X1), school participation rate 7-12 years (X2), informal sector workers (X3), open unemployment rate (X4), household using bamboo walls (X5), and household users of inadequate drinking water sources (X6). The result of this research is the best model to model the percentage of poor people is Spatial Error Model (SEM) with spatial weighting matrix Queen Contiguity and obtained AIC value 191,02 and R2 equal to 77,47%. Factors that have significant effect on the percentage of the poor are school enrollment (X2), informal sector workers (X3), household users of inadequate drinking water sources (X6) and there is an error dependency on one location to another.","PeriodicalId":118320,"journal":{"name":"J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126832585","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}