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

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.