NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA

  • Ani Budi Astuti Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Abdi Negara Guci Department of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Viky Iqbal Azizul Alim Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Laila Nur Azizah Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Meirida Karisma Putri Department of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Wigbertus Ngabu Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss3pp1663-1672
Keywords: Cluster Analysis, Elbow, Oil Subsidies, Shilouette, Inflation, K-Means

Abstract

The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.

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Published
2023-09-30
How to Cite
[1]
A. Astuti, A. Guci, V. Alim, L. Azizah, M. Putri, and W. Ngabu, “NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA”, BAREKENG: J. Math. & App., vol. 17, no. 3, pp. 1663-1672, Sep. 2023.
Issue
Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Ani Budi Astuti, Abdi Negara Guci, Viky Iqbal Azizul Alim, Laila Nur Azizah, Meirida Karisma Putri; Wigbertus Ngabu

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