DOI: https://doi.org/10.14710/j.gauss.10.1.76-84

PERBANDINGAN METODE OPTIMASI UNTUK PENGELOMPOKAN PROVINSI BERDASARKAN SEKTOR PERIKANAN DI INDONESIA (Studi Kasus Dinas Kelautan dan Perikanan Indonesia)

*Edy Sulistiyawan  -  Program Studi Managemen, Fakultas Ekonomi dan Bisnis, Universitas PGRI Adi Buana Surabaya, Indonesia
Alfisyahrina Hapsery  -  Program Studi Statistika, Fakultas Sains dan Teknologi, Universitas PGRI Adi Buana Surabaya, Indonesia
Lucky Junita Ayu Arifahanum  -  Program Studi Statistika, Fakultas Sains dan Teknologi, Universitas PGRI Adi Buana Surabaya, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

The fisheries sector has an important role in supporting the food security chain, where the world's protein needs can be met by fisheries resources, both from capture fisheries and aquaculture. There are several fisheries sectors including fishing companies, capture fisheries production, number of ships, types and size of cultivated land. Therefore a statistical analysis is needed to increase the potential of fisheries in Indonesia. Data on the fisheries sector used in this study from the Indonesian Central Statistics Agency in 2018, which included the 2016 fisheries sector with 34 observation units in Indonesia. By using cluster analysis K-Means aims to group provinces in Indonesia based on the fisheries sector so that several groups are formed which will show the characteristics of each group. There are three determinations of the optimum number of clusters, namely the Elbow method, Silhouette method, and GAP Statistics. The results showed that optimum clusters were formed in 2 clusters, with the best Elbow and Silhouette methods. Where the first cluster is a region that shows a low value of the fisheries sector consisting of 30 provinces this is due to inadequate infrastructure and use that is not optimal while cluster 2 regions that have great potential in the Indonesian fisheries sector in 2016 as many as 4 provinces namely West Java, Java Central, East Java, and South Sulawesi as dominating capture fisheries production and aquaculture.

 

Keywords: Fisheries Sector, K-Means Cluster Analysis, Elbow Method, Silhoutte Method and GAP Statistics.

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Keywords: Fisheries Sector, K-Means Cluster Analysis, Elbow Method, Silhoutte Method and GAP Statistics.

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  1. Ediyanto, Mara M.N., Satyahadewi N., (2013), Pengklasifikasian Karakteristik Dengan Metode K-Means Cluster Analysis, Bulletin Ilmiah Matematika Statistika dan Terapannya, 2(2): 133-136
  2. FAO Fisheries Departement, (2006), State of world aquaculture 2006, FAO Fisheries Technical Paper, No.500. Rome, 134p
  3. Kementerian PPN, (2014), Kajian Strategi Pengelolaan Perikanan Berkelanjutan, Jakarta: Direktorat Kelautan dan Perikanan 2014
  4. Prayoga, A. & Zain, I. (2015). Analisis Faktor dan Pengelompokan Kecamatan berdasarkan Indikator Mutu Pendidikan Jenjang Pendidikan Dasar di Kabupaten Sidoarjo. Surabaya: Institut Teknologi Sepuluh Nopember
  5. Supranto, J., (2004), Analisis Multivariat Arti dan Interpretasi, Jakarta: Rineka Cipta
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