Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province

M. Y Matdoan; Nur A. Purnamasari; Novita S. Laamena

doi:10.30598/pijmathvol2iss2pp63-70

M. Y Matdoan Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Pattimura, Indonesia
Nur A. Purnamasari Statistics Study Program, Faculty of Mathematics and Natural Sciences, Mataram University, Indonesia
Novita S. Laamena Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Pattimura, Indonesia

DOI: https://doi.org/10.30598/pijmathvol2iss2pp63-70

Keywords: Clusterization K-Means; Fishery

Abstract

Maluku Province has large natural resources with various potentials, from the ocean floor to the mainland. Capture fishery products are one of the leading sectors that contribute greatly to the GRDP of Maluku Province. The K-Means clustering algorithm is a suitable algorithm for grouping data objects that have the same identity. The purpose of this study is to cluster districts/cities in Maluku Province based on capture fishery products. The type of data in this study is secondary data sourced from the Maluku Province Central Bureau of Statistics (BPS) Publication in 2022. The result is that there are 3 districts/cities clusters in Maluku Province based on capture fishery products. Cluster 1 with the category of sufficient capture fisheries products, namely the Districts of Tanimbar Islands, Buru, East Seram, West Seram, South Buru, Southwest Maluku, Ambon City and Tual City. Furthermore, Cluster 2 with the category of many capture fishery products, namely the Aru Islands Regency and Southeast Maluku Regency. Furthermore, for Cluster 3, the category of capture fishery products is very large, namely Central Maluku Regency.

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