DOI: https://doi.org/10.14710/medstat.15.2.220-230

APPLICATION OF BIPLOT ANALYSIS WITH ROBUST SINGULAR VALUE DECOMPOSITION TO POVERTY DATA IN SULAWESI ISLAND

*Febriyana Taki  -  Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119, Indonesia
Lailany Yahya  -  Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119, Indonesia
Muhammad Rezky Friesta Payu  -  Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Poverty is defined as an inability of the individual to meet basic needs for a decent life. According to BPS data in
2020, Sulawesi Island ranks fifth as the poorest island in Indonesia. This study aims to find out the mapping of areas and indicators of poverty in Sulawesi Island using Biplot Analysis with Robust Singular Value Decomposition approach for outlier research data. Based on the results of the study, there are five objects that are outlier and the information provided by the biplot amounted 98.45%. District/city that have similar characteristics are divided into 4 groups. The indicator of poverty that has the most diversity is the School Old Expectations Numbers (Var 4) and the one with the least diversity is Poor Households Using Clean Water (Var 8). Indicators of poverty that are positively correlated are Literacy Numbers (Var 1) and Non-Working Poor Population (Var 5), while the negative correlated are The Non-Working Poor Population (Var 5) and Poor Households Using Clean Water (Var 8). There are 19 districts/cities that have literacy values above the average of all districts/cities and 11 districts/cities that have a per capita expenditure value below the average of all districts/cities.

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Keywords: Poverty; Biplot Analysis; Robust Singular Value Decomposition; Outlier

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