Advance Search

Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan

DOI: https://doi.org/10.20956/ejsa.v5i1.13605
Article History

Submited : April 20, 2021
Published : January 29, 2024

Keywords:
Cluster analysis is a multivariate analysis technique that aims to cluster the observational data or variables into clusters in such a way that each cluster is homogeneous according to the factors used for clustering. This study used the Centroid linkage algorithm that was useful for forming groups based on the distance between centroids and the K-Medoids algorithm that was based on the use of the most centered object (medoid) to group districts/cities and obtained comparison results based on the education indicator data in South Sulawesi. The implementation of the Centroid Linkage Algorithm and K-Medoids on the education indicator data in South Sulawesi in 2018, showed that the grouping of districts/cities in South Sulawesi produced 2 clusters with cluster 1 of 21 districts/cities, and cluster 2 of 3. To determine the best method, it was seen from the value of the Standard Deviation ratio in the cluster 〖(S〗_W) and Standard Deviation between Clusters 〖(S〗_B) showed the same standard deviation ratio (S) in the Centroid Linkage algorithm and K-Medoids that was equal to 104,967.

References

  1. Badan Pusat Statistik. Indikator Kesejahteraan Rakyat Provinsi Sulawesi Selatan 2019. Sulawesi Selatan. 2019.
  2. Rachmatin, D. Aplikasi Metode-metode Agglomerative dalam Analisis Cluster pada Data Tingkat Polusi Udara. Infinity Jurnal Ilmiah Program Studi Matematika STKIP Siliwangi, 3(2), 2014.
  3. Setiyawati, A. W. Implementasi Algoritma Partitioning Aroud Medoids (PAM) untuk Pengelompokan Sekolah Menengah Atas di DIY berdasarkan Nilai Daya Serap Ujian Nasional. Yogyakarta: Universitas Sanata Dharma. 2017.
  4. Gudono, P. Analisis Data Multivariat Edisi Pertama. Yogyakarta: BPFE-YOGYAKARTA. 2011.
  5. Mongi, C. E. Penggunaan Analisis Two Step Clustering untuk Data Campuran. JdC, 4(1), 2015.
  6. Karlita, T. Algoritma Perbaikan Penentuan Titik Pusat Awal Berbasis Hirarki untuk Klasterisasi Data Katerogikal. Surabaya: Institut Teknologi Sepuluh November. 2006.
  7. Vercillis, Carlo. Business Intelligence: Data Mining and Optimization for Decision Making. Milan: WILEY. 2009.
  8. Dini Marlina, N. F. Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak. Jurnal CoreIT, 4(2), 2018.
  9. Johnson, R.A., Wichern, D. W. Applied Multivariate Statistical Analysis 6th Edition. New Jersey: Prentice Hall. 2007.
  10. Nicolaus, E. S. Penentua Jumlah Cluster Optimal pada Median Linkage dengan Indeks Validitas Silhouette. Buletin Ilmiah Math. Stat. dan Terapannya (Bimaster), 5(2), 2016.
  11. Laeli, S. Analisis Cluster dengan Average Linkage Method dan Ward’s Method untuk Data Responden Nasabah Asuransi Jiwa Unit Link . Yogyakarta: Universitas Negeri Yogyakarta. 2014.

Downloads

Download data is not yet available.
Fulltext
pdf
statcounter