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Tuti Purwaningsih
Department of Statistics, Universitas Islam Indonesia, Yogyakarta, Indonesia
Economics, Econometrics & Finance

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Application of Spatial Regression Model for Modeling Measles Case in Indonesia Tuti Purwaningsih; Mutiara Herawati; Nanda Hadina Wijayanti
International Journal of Applied Business and Information Systems Vol. 2 No. 1 (2018)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (477.811 KB) | DOI: 10.31763/ijabis.v2i1.124

Abstract

Measles is also known as morbili in Latin and measles in English. Measles, in the past is considered as something that must be experienced by every child, they assume, that measles can heal itself if it was already out, so that children with measles do not need to be treated. This study examines the case of measles and the causes of measles. The variables used in the study were cases of measles (Y), population density (X1), immunization coverage (X2), average incidence (X3), and number of deaths (X4) in Indonesia covering all provinces. The study examined the pattern of spread, then given a SEM application to identify how much influence the measles factor can affect the case of measles in Indonesia. The results of the study show that Measles Cases in Indonesia have a regional grouping pattern. The modeling results using SEM show lambda and all significant variables. The SEM model produced AIC of 462,429 which was better than the regression of the SLM model with AIC of 467,499.
Building Model of Flood Cases in Central Java Province using Geographically Weighted Regression (GWR) Tuti Purwaningsih; Citra Saktian Prajaningrum; Mai Anugrahwati
International Journal of Applied Business and Information Systems Vol. 2 No. 2 (2018)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (668.082 KB) | DOI: 10.31763/ijabis.v2i2.168

Abstract

Indonesia is one of the countries hit by many disasters. During the last five years the disaster increased in the last year, namely in 2016. In terms of the types of disasters, most were floods. The flood disaster has the highest incidence rate in Central Java Province. Flood is a natural phenomenon where there is excess water which is not accommodated by drainage in an area. To be able to identify the risk of flooding that affects humans and their environment, it is necessary to know the causes. The causes of flooding can come from natural and non-natural factors. Seeing the high incidence of flooding in Central Java, the authors drew attention to research whether the factors that influence flooding in the province and how to model it by looking at the spatial effects in it using Geographically Weighted Regression (GWR) analysis. The results showed that the incidence of flooding using GWR analysis had four significant variables, namely rainfall, rainy days, humidity and area. From the model obtained, it has R2 of 56%, and has as many as six models of variables that are significant for each region.
Co-Authors Citra Saktian Prajaningrum Mai Anugrahwati Mutiara Herawati, Mutiara Nanda Hadina Wijayanti