GRDP Growth Rate Clustering in Surabaya City uses the K-Means Algorithm

Nur Ahlina Febriyati, Achmad Daengs GS, Anjar Wanto


Gross Regional Domestic Product (GRDP) is an indicator used to measure economic performance in a period. GRDP is the amount of added value generated by all business units in a particular area. It can also be said to be the sum of the value of the final goods and services produced by all economic units. Therefore, this study aims to cluster the GRDP Growth Rate according to business fields in the city of Surabaya, so that it is known which sectors have high or low growth. The clustering algorithm used is K-Means. By using this method, the data will b,e grouped into several clusters, where the implementation of the K-Means Clustering process uses the Rapid Miner tools. The data used is the GRDP Growth Rate in Surabaya City by Business Field, 2010-2019 (Percent). The data is divided into 3 clusters: high, medium, and low. The results obtained are nine categories/sectors with high clusters, 5 categories / sectors with medium clusters, and three categories,s / sectors with low clusters. This can be input and information for the Surabaya City government to further maximize efforts to increase the GRDP Growth Rate in the area.

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