Article Sidebar

Submitted
Nov 7, 2021
Accepted
Dec 30, 2021
Published
Dec 31, 2021
Download
PDF
Statistic
Read Counter : 203 Download : 188

Main Article Content

Abstract

Fisheries are one of the agricultural sub-sectors that play an important role in contributing to income figures for the state and the region because most of Indonesia's territory is water so that the fisheries sector is a sub-sector that is feasible to be developed in this country, one of which is through aquaculture. One of the efforts that can increase and maintain productivity in the aquaculture sector is to classify provinces that produce aquaculture production into groups based on the similarity of characteristics possessed by each province in Indonesia. In this study, clustering was carried out using cluster analysis using the average linkage method and based on the analysis results obtained showed that cluster 1 consists of 25 provinces, cluster 2 consists of 5 provinces, cluster 3 consists of 2 provinces, cluster 4 consists of 1 province, and cluster 5 consists of 1 province with a standard deviation value within a cluster of 11,729 and a standard deviation between clusters of 118,745.

Keywords

clustering average linkage productivity aquaculture Indonesia

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Putera, F. H. A., Mangitung, S. F., Madinawati, & Handayani, L. (2021). Clustering of Province in Indonesia Based on Aquaculture Productivity Using Average Linkage Method. Parameter: Journal of Statistics, 2(1), 16-20. https://doi.org/10.22487/27765660.2021.v2.i1.15683

References

  1. Amaliyah, F., and Wibawati. (2012). Grouping of Regencies/Cities in East Java Based on Healthy Indonesia Indicator 2010. Journal of Science and Art ITS, 1(1), 188-193
  2. Fadliana, A., and Rozi, F. (2015). Penerapan Metode Agglomerative Hierarchical Clustering untuk Klasifikasi Kabupaten/Kota di Provinsi Jawa Timur Berdasarkan Kualitas Pelayanan Keluarga Berencana. Journal of Cauchy, 4(1), 25-40
  3. Goreti, M., Novia, Y., and Wahyuningsih, S. (2016). Comparison of Cluster Analysis Results Using Single Linkage Method and C-Means Method. Journal of Exponential, 7(1), 9-16
  4. Gudono. (2015). Multivariate Data Analysis, Four Edition. Yogyakarta: BPFE.
  5. Hardius, U., and Nurdin, S. (2013). Application of Multivariate Techniques for Marketing Research. Jakarta: Raja Grafindo Persada.
  6. Nurlia. (2009). Peranan Sub Sektor Perikanan terhadap Produk Domestik Regional Bruto (PDRB) dan Kesempatan Kerja di Kabupaten Pinrang Periode 2005-2009. Makassar: Hasanuddin University.
  7. Pusdatin [KKP]. (2013). Marine and Fisheries in Figures 2013. Jakarta: Center for Data, Statistics and Information, Ministry of Marine Affairs and Fisheries.