Performance of K-Means and DBSCAN Algorithm in Clustering Gross Regional Domestic Product

Jonathan K. Wororomi, Caecilia Bintang Girik Allo, Nicea Roona Paranoan, Wickly Gusthvi

Abstract


Gross Regional Domestic Product (GRDP) is one of important indicator to determine the economic conditions of a region. GRDP are obtained from sum of value added produced by all unit of production in a region. This study use GRDP by production approach that grouped into seventeen categories of Industry. The government always put the big efforts to increase the economic growth after Covid-19 pandemic. According publication of BPS - Statistics Indonesian, in the year of 2021 and 2022 it’s growth between 3.70% and 5.31%. The aim of these study are determined the cluster GDRB based on province in Indonesia at current prices and analyses the performance of the cluster method. The results showed that by using the DBSCAN, two clusters were formed and one province can be detected as an outlier. On the other hands, performance of the method by K-Means showed two clusters. The silhouette value using K-Means is higher than the DBSCAN. For these case, the performance of K-Means is more appropriate than DBSCAN to use in clustering province in Indonesia based on GRDP at Current Market Prices. Moreover, performance of DBSCAN shows more sensitive on outliers detection.

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References


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DOI: https://doi.org/10.32535/jicp.v6i5.2710

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