PENGOLAHAN DATA MINING TERKAIT DATA EKSPOR PERIKANAN INDONESIA 2019-2020 DENGAN UJI REGRESI

Elgin Yuzhar Areta

Abstract

Fish export data mining (or fisheries export data mining) refers to the process of information mining or extracting useful patterns from available fish export data. The purpose of mining fish export data is to identify patterns, trends, and insights that can assist in making decisions regarding fish exports. This study uses a regression test to analyze the relationship between export weight and export value in the context of fisheries. Fishery export data based on Main Target Countries from 2012 to 2019 were obtained from the Directorate General of Customs and Excise. The results of the regression test show that both constants have positive values, indicating that if the weight of exports increases, the value of exports will also increase. The weight constant in 2019 shows that every increase in weight per unit (ton) will result in an increase in export value of 17.66%, while the constant weight in 2020 shows an increase in export value of 16.02% per increase in weight per unit (ton), grouping was carried out using the K-Means algorithm with three clusters. The grouping results show that China and Malaysia are included in the export group with a high level (C1), Vietnam and Thailand are included in the export group with a moderate level (C2), and Hong Kong, Singapore, Nigeria, India, Japan, and Arab Emirates are included in the group. low-level exports (C3). This research provides an understanding of the relationship between export weight and export value in the fisheries sector and identifies countries that have high, medium, and low export levels.

Keywords

Mining Export; Clustering; Fisheries;

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