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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Utama, Rafi Ilmi Badri
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Grouping of Districts Based on Poverty Factors in Papua Province Uses The K-Medoids Algorithm Novianti, Afdelia; Afnan, Irsyifa Mayzela; Utama, Rafi Ilmi Badri; Widodo, Edy
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.691 KB) | DOI: 10.20885/enthusiastic.vol1.iss2.art6

Abstract

Poverty is an essential issue for every country, including Indonesia. Poverty can be caused by the scarcity of basic necessities or the difficulty of accessing education and employment. In 2019 Papua Province became the province with the highest poverty percentage at 27.53%. Seeing this, the district groupings formed in describing poverty conditions in Papua Province are based on similar characteristics using the variables Percentage of Poor Population, Gross Regional Domestic Product, Open Unemployment Rate, Life Expectancy, Literacy Rate, and Population Working in the Agricultural Sector using K-medoids clustering algorithm. The results of this study indicate that the optimal number of clusters to describe poverty conditions in Papua Province is 4 clusters with a variance of 0.012, where the first cluster consists of 10 districts, the second cluster consists of 5 districts, the third cluster consists of 12 districts, and the fourth cluster consists of 2 districts.
Co-Authors Afnan, Irsyifa Mayzela Edy Widodo Novianti, Afdelia