CLUSTERING PRODUKSI PERIKANAN BUDIDAYA LAUT BERDASARKAN PROVINSI MENGGUNAKAN ALGORITMA K-MEANS

Alfian Firlansyah (Universitas Negeri Makassar)
Andi Akram Nur Risal (Universitas Negeri Makassar)
Fhatiah Adiba (Universitas Negeri Makassar)
Andi Baso Kaswar (Universitas Negeri Makassar)

Journal of Embedded Systems, Security and Intelligent Systems
Vol. 2 No. 1 (2021): Vol 2, No 1 (2021): May 2021

Indonesia is a large maritime country, and most of its territorial waters are larger than its land area. Due to the vastness of the oceans, the large number of large and small islands makes Indonesia a potential area for marine cultivation. In general, the existing data based on the Central Statistics Agency (BPS) of Marine Aquaculture Production for each province in Indonesia only applies to production data which only produces detailed data on total marine aquaculture production in tonnes per year, and takes a long time. To classify very large data, a method is needed that can use the K-Means algorithm to classify the highest, middle, and lowest opportunities in the field of marine aquaculture from 2004 to 2018. The results implemented in python consisted of 26 provinces in klaster 1 (C1), 3 provinces in klaster 2 (C2), and 5 provinces in klaster 3 (C3).