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Journal : IJASDS: Indonesian Journal of Applied Statistics and Data Science

Pengklasifikasian 10 Kabupaten/Kota di Provinsi Nusa Tenggara Barat untuk Kasus Kemiskinan Tahun 2022 Menggunakan Analisis Cluster Metode K-Means Sabina, Sabna Zulfaa; Alfarez, Dzaki Ade; Graha, Syifa Salsabila Satya; Auladi, Muhammad Yuzaul; Lisa , Harsyiah; Lisa Harsyiah
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5415

Abstract

Poverty is a very serious problem for countries in the world, especially for developing countries like Indonesia. Poverty will have a big impact if it occurs in the long term with different factors. One province that is still in the spotlight for high levels of poverty is West Nusa Tenggara Province. Even though the number of poor people in West Nusa Tenggara Province has decreased, conditions of the ground show that there are still many people whose lives are far from decent. Therefore, the government must immediately find a solution to overcome the problem of poverty. To overcome cases of poverty in a region, we can group the characteristics of these regions based on poverty indicators into several clusters. Grouping in this case is carried out with data that will be analyzed using the K-Means cluster analysis method. So the results obtained by analysis using the K-Means cluster method for grouping 10 regencies/cities in West Nusa Tenggara Province based on poverty in 2022 formed 3 clusters, namely cluster 1 consisting of West Sumbawa Regency, Bima City and Mataram City, cluster 2 consisting of Bima Regency, Dompu Regency, West Lombok Regency, Central Lombok Regency, East Lombok Regency, and Sumbawa Regency, and cluster 3 consists of North Lombok Regency. Apart from that, the characteristics of each cluster were also obtained, namely cluster 1 containing the districts/cities with the highest PPM values. While RLS, AHH, and TPT have very high numbers in 2022, cluster 2 contains districts/cities that have quite low PPM, RLS, AHH, and TPT numbers in 2022, and cluster 3 contains a group of districts/cities with RLS, AHH, and TPT has quite low numbers compared to the high PPM in 2022.