Poverty Level Grouping in West Java Province with the K-Means Clustering Method

Pengelompokan Tingkat Kemiskinan di Provinsi Jawa Barat dengan Metode K-Means Clustering

Authors

  • Amelia Universitas Muhammadiyah Semarang
  • Indah Manfaati Nur Universitas Muhammadiyah Semarang
  • Muhammad Rizky Universitas Muhammadiyah Semarang
  • Septiana Putri Milasari Universitas Muhammadiyah Semarang

DOI:

https://doi.org/10.26714/jodi.v1i2.152

Keywords:

Poverty, K-Means Clustering, Jawa Barat

Abstract

Poverty in a region will have an impact on hampering national development. Poverty is an economic disease that is often faced by every country, including Indonesia. According to information obtained from the Central Bureau of Statistics, we can gather data on the poverty rate in all provinces of Indonesia, with a particular focus on the province of West Java. West Java province is one of the provinces with the highest population density on the island of Java, which is ranked 2nd after the province of DKI Jakarta and ranks 4th for the province with a high percentage of poor people after DI. Yogyakarta, Central Java, and East Java. Consequently, it is crucial for the regional government to identify areas with high, moderate, or low poverty rates. This information will enable the local government to formulate appropriate policies and prioritize interventions to address poverty effectively. In this study, the K-Means clustering method was used to classify poverty rates based on two variables, namely the community development index and the open unemployment rate using the help of RStudio software. The findings indicated that the application of the elbow method in West Java province resulted in the identification of three distinct clusters of districts/cities that stood out as the most prominent. Cluster 1 (districts/cities with relatively high poverty rates), cluster 2 (districts/cities with low poverty rates), cluster 3 (districts/cities with high poverty rates). Regencies/cities that fall into the category with a high poverty rate are Sukabumi, Cianjur, Garut, Tasikmalaya, Ciamis, Kuningan, Cirebon, Majalengka, Indramayu, Subang, West Bandung, and Pangandaran.

Metrics

Metrics Loading ...

Downloads

Published

2023-12-12

How to Cite

Amelia, Nur, I. M., Rizky, M., & Milasari, S. P. (2023). Poverty Level Grouping in West Java Province with the K-Means Clustering Method: Pengelompokan Tingkat Kemiskinan di Provinsi Jawa Barat dengan Metode K-Means Clustering. Journal Of Data Insights, 1(2), 51–61. https://doi.org/10.26714/jodi.v1i2.152