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Febriana Sulistya Pratiwi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro

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PENERAPAN RESPONSE BASED UNIT SEGMENTATION IN PARTIAL LEAST SQUARE (REBUS-PLS) UNTUK ANALISIS DAN PENGELOMPOKAN WILAYAH (Studi Kasus: Kesehatan Lingkungan Perumahan di Provinsi Jawa Tengah) Febriana Sulistya Pratiwi; Sudarno Sudarno; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28927

Abstract

Residental environmental health is a complex problem that depends on several dimensions. One of the statistical method that can be used to analyze the relation between complex dimensions is Structural Equation Modeling (SEM) with a variant/component based approach or Partial Least Square. The purpose of this study is to develop a structural model of the relation between household economy, education, housing facilities, and residental environmental health in Central Java Province in 2018 based on 12 valid and reliable indicators. In the structural equation model there is a significant positive effect path that is the influence of household economy towards education and towards housing facilities, and influence housing facility on the residential environment health. In SEM analysis it is generally assumed that the data taken comes from a homogeneous population but often the data consists of several segments. Therefore, we need a method to detect heterogeneity problems, namely Response Based Unit Segmentation in Partial Least Square (REBUS-PLS). Based on the dendogram produced, by forming 2 classes/segments,  values as the accuracy of the prediction model on the local model had a higher value (except  values for Education in local model 2) than  values on the global model. In addition, the Goodnes of Fit value as a measure of model suitability for each local model is also had a higher value, so that it indicates the goodness of the model in the local model is better than the global model.Keywords: environmental health, SEM, PLS, REBUS-PLS