Penentuan Cluster Optimum pada Tingkat Pengangguran dan Tingkat Kemiskinan di Jawa Timur Menggunakan Algoritma Fuzzy C-Means

  • Ari Eko Wardoyo Universitas Muhammadiyah Jember
  • Nigati Tripuspita Universitas Muhammadiyah Jember
Keywords: Clustering Analysis, Optimization, Fuzzy C-Means, Unemployment Data, Poverty Data

Abstract

There are many methods used in resolving data clustering. One of them is the Fuzzy C-Means (FCM) method, which is a reliable method to solve clustering problems in the East Java region. This study aims to determine the optimum cluster in the East Java region which can help the government to identify problems and assist policymaking in regencies/cities in East Java province. The research process uses data from the central statistical agency, namely the unemployment rate and poverty rate from 2010 to 2015. In this study, the Davies Bouldin Index (DBI) is used as a cluster validation test for determining the optimum cluster. Unemployment rate and poverty rate data were analyzed using RStudio. From the calculation of the FCM method and also the determination of the optimum cluster results obtained in 2 clusters with a DBI value of 1.2759, 3 clusters with a DBI value of 0.9937, 4 clusters with a DBI value of 0.8737. The optimum cluster is in 4 clusters with a minimum DBI value.

References

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Published
2021-02-11
Section
Articles