Clustering Regencies/Cities in Kalimantan Island Based on Poverty Indicators using Agglomerative Hierarchical Clustering (AHC)

Authors

  • Ludia Ni'matuzzahroh Institut Teknologi Sepuluh Nopember
  • Andrea Tri Rian Dani Universitas Mulawarman
  • Narita Yuri Adrianingsih Universitas Tribuana Kalabahi

Keywords:

Agglomerative Hierarchical Clustering, Cluster, Poverty

Abstract

Cluster analysis is a statistical analysis that can group objects of observation into several groups/clusters based on their similarity of characteristics. The grouping into several clusters is based on the information contained in the object under study. A cluster can be said to be good if it has high internal homogeneity and high external heterogeneity. The clustering method used in this study is the agglomerate hierarchical clustering (AHC) method, where the cluster formation algorithm used in this AHC method is average linkage, single linkage, complete linkage, and ward. Cluster analysis using the AHC method will be applied to poverty indicator data for Regencies/Cities in Kalimantan Island, which consists of several variables. This study aims to obtain the optimal results of grouping Regencies/Cities in Kalimantan Island, with the number of clusters that have been determined at the beginning, namely as many as 3 clusters. Based on the results of the analysis using the AHC method, the ward algorithm produces an agglomerate coefficient value of 0.89, where this value is close to 1, which means that the ward algorithm is the best in clustering Regencies/Cities in Kalimantan Island.

Downloads

Download data is not yet available.

References

BPS., 2021. DATA DAN INFORMASI KEMISKINAN KABUPATEN/KOTA. Badan Pusat Statistik, Jakarta.

BPS Provinsi Kalimantan Barat., 2021. Kalimantan Barat dalam Angka 2021. Badan Pusat Statistik, Kalimantan Barat.

BPS Provinsi Kalimantan Selatan., 2021. Kalimantan Selatan dalam Angka 2021. Badan Pusat Statistik, Kalimantan Selatan.

BPS Provinsi Kalimantan Tengah., 2021. Kalimantan Tengah dalam Angka 2021. Badan Pusat Statistik, Kalimantan Tengah.

BPS Provinsi Kalimantan Timur., 2021. Kalimantan Timur dalam Angka 2021. Badan Pusat Statistik, Kalimantan Timur.

BPS Provinsi Kalimantan Utara., 2021. Kalimantan Utara dalam Angka 2021. Badan Pusat Statistik, Kalimantan Utara.

Dani, A. T. R., Wahyuningsih, S., and Rizki, N. A., 2019. Penerapan Hierarchical Clustering Metode Agglomerative Pada Data Runtun Waktu. Jambura Journal of Mathematics, Vol. 1, No. 2, 64-78.

Dani, A. T. R., Wahyuningsih, S., and Rizki, N. A., 2020. Pengelompokan Data Runtun Waktu Menggunakan Analisis Cluster (Studi Kasus: Nilai Ekspor Komoditi Migas dan Nonmigas Provinsi Kalimantan Timur Periode Januari 2000-Desember 2016). Jurnal EKSPONENSIAL, Vol. 11, No. 1, 29-37.

Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E., 2010. Multivariate Data Analysis, 7th Edition. Pearson Prentice Hall, New Jersey.

Jain, A.K. & Dubes, R.C., 1988. Algorithms for Clustering Data. Prentice-Hall, Inc., Upper Saddle River, New Jersey.

Johnson, R.A. & Wichern, D.W., 2007. Applied Multivariate Statistical Analysis, 6th Edition. Prentice Education, Inc., New Jersey.

Kamalha, E., Kiberu, J., Nibikora., I., Mwasiagi, J. I., and Omollo, E., 2017. Clustering and Classification of Cotton Lint Using Principle Component Analysis, Agglomerative Hierarchical Clustering, and K-Means Clustering. Journal of Natural Fibers, 1-11. DOI: 10.1080/15440478.2017.1340220

Kaufman, L. & Rousseeuw, P.J., 1990. Finding Groups in Data an Introduction to Cluster Analysis. John Wiley & Sons Inc Publication, New Jersey.

Liu, N., Xu, Z., Zeng, X. J., and Ren, P., 2021. An Agglomerative Hierarchical Clustering Algorithm for Linear Ordinal Rankings. Information Sciences, 170-193.

Novidianto, R. and Dani, A. T. R., 2020. Analisis Klaster Kasus Aktif Covid-19 Menurut Provinsi di Indonesia Berdasarkan Data Deret Waktu. Journal of Statistical Application and Computational Statistics, Vol. 12, 15-24.

Nurseptiani, A., Satria, Y., and Burhan, H., 2020. Application of Agglomerative Hierarchical Clustering to Optimize Matching Problems in Ridesharing for Maximize Total Distance Savings. Journal of Physics: Conference Series, 1-7. DOI: 10.1088/1742-6596/1821/1/012016

Prasetyo, E., 2012. Data Mining: Konsep dan Aplikasi Menggunakan MATLAB. Penerbit Andi, Yogyakarta.

Santosa, B., 2007. Data Mining: Teknik Pemanfaatan Data untuk Memprediksi Kriteria Nasabah Kredit. Jurnal Komputer dan Informatika, Vol. 1, No. 1, 53-57.

Zahrotun, L., 2015. Analisis Pengelompokan Jumlah Penumpang Bus Trans Jogja Menggunakan Metode Clustering K-Means dan Agglomerative Hierarchical Clustering (AHC). JURNAL INFORMATIKA, Vol. 9, No. 1, 1039-1047.

Downloads

Published

2022-09-07

Issue

Section

Research Articles