Grouping The Regencies/Cities in Indonesia Based on Expenditure Groups Inflation Value Using DBSCAN Method

Penulis

  • Meliani Putri Universitas Negeri Padang
  • Dony Permana
  • Syafriandi Syafriandi
  • Zilrahmi

DOI:

https://doi.org/10.24036/ujsds/vol1-iss3/61

Kata Kunci:

DBSCAN, Expenditure Group Inflation, Regencies/Cities in Indonesia, Silhouette Coefficient

Abstrak

Inflation is one of the important problems faced by a country in achieving economic goals and targets. The amount of inflation is measured using the Consumer Price Index for eleven expenditure groups in 90 regencies/cities in Indonesia. The occurrence of differences in inflation rates between regencies/cities in Indonesia will affect Indonesia's national inflation. The purpose of this research is to grouping regencies/cities based on expenditure groups inflation value and to identify the characteristics of the resulting groups. DBSCAN is a density-based non-hierarchical cluster method that can be used in data conditions that contain outliers. The data used in this study is secondary data obtained from the publication of the Badan Pusat Statistik Republic of Indonesia (BPS RI) regarding inflation by expenditure group. The analysis includes outlier detection, grouping using the DBSCAN method, performing cluster validation with silhouette coefficient, and identifying the characteristics of the clusters formed. Based on the grouping that has been done, two clusters are produced with a silhouette coefficient value of 0.65. The resulting cluster is cluster 0 in the form of a noise cluster consisting of 3 regencies/cities with regencies/cities that have a high category expenditure group inflation rate. Cluster 1 consisting of 87 regencies/cities is a cluster with regencies/cities that have a low category expenditure group inflation rate.

Unduhan

Diterbitkan

2023-05-31

Cara Mengutip

Meliani Putri, Dony Permana, Syafriandi Syafriandi, & Zilrahmi. (2023). Grouping The Regencies/Cities in Indonesia Based on Expenditure Groups Inflation Value Using DBSCAN Method. UNP Journal of Statistics and Data Science, 1(3), 164–171. https://doi.org/10.24036/ujsds/vol1-iss3/61

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