Classification of Human Development Index Using K-Means

Retno Tri Vulandari, Sri Siswanti, Andriani Kusumaningrum Kusumawijaya, Kumaratih Sandradewi


Human development progress in Central Java. It is characterized by a continued rise in the human development index (HDI) of Central Java. HDI is an important indicator for measuring success in the effort to build the quality of human life. HDI explains how residents can access the development results in obtaining a long and healthy life, knowledge, education, decent standard of living and so on. HDI is affected by four factors, namely life expectancy, expected years of schooling, means years of schooling, and expenditure per capita. Currently the Central bureau of statistics do grouping HDI, using calculation formula then known how the value HDI each regency or city in Central Java. In this research we classified the regency or city in Central Java based on the HDI be high, middle, and under estimate area. We used cluster analysis. Cluster analysis is a multivariate technique which has the main purpose to classify objects based on their characteristics. Cluster analysis classifies the object, so that each object that has similar characteristics to be clumped into a single cluster (group). One of the cluster analysis method is k-means. The result of this research, there are three groups, high estimate area, middle estimate area, and under estimate area. The first group or the under estimate area contained 12 regencies, namely Cilacap, Purbalingga, Purworejo, Wonosobo, Grobogan, Blora, Rembang, Pati, Jepara, Demak, Pekalongan, and Brebes. The second group or the middle estimate area contained 8 regencies, namely Banjarnegara, Kebumen, Magelang, Temanggung, Wonogiri, Batang, Pemalang, and Tegal. The third group or the high estimate area contained 11 regencies, namely Banyumas, Kudus, Boyolali, Klaten, Sukoharjo, Karanganyar, Sragen, Semarang, Kendal, Surakarta, and Salatiga.

Keywords : cluster analysis, k-means, the human development index.

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Yunita, M. Analisis Hubungan antara Pertumbuhan Ekonomi dengan Indeks Pembangunan Manusia. ITS. Surabaya. 2007.

Klawonn, K. Fuzzy Clustering and Fuzzy Rule. Science Journal. 245-252. 1997.

Widodo. Perbandingan Metode Fuzzy C-Means dan Fuzzy C-Shell Clustering (Kasus Kabupaten Kota Pulau Jawa berdasarkan variabel pembentuknya). ITS. Surabaya. 2012.

Widodo, S. Lung Field Segmentation on Computed Tomography Image using Active Shape Model. Kursor Journal, 7(2): 99-108. 2013.

Oyelade, O. Application of K-Means Clustering Algorithm for Prediction of Students Academic Performance. International Journal of Computer Science and Information Security. 7(1): 292-295. 2010.

Siska, S. T. Analisa dan Penerapan Data Mining untuk menentukan Kubikasi Air Terjual berdasarkan Pengelompokan Pelanggan menggunakan Algoritma K-Means Clustering. Jurnal Teknologi Informasi dan Pendidikan. 9: 86-93. 2016.

Bunkers, J. Definition of Climate Regions in the Northen Plains using an Objective Cluster Modification Technique. Journal of Climate. 9: 130-146. 1996.

Pethalakshmi, A. Modification in K-Means Clustering Algorithm Through Affinity Measure to Increase The Cluster Uniqueness. International Journal on Soft Computing. 1(2). 2012.

Goryawala, M. Analyzed on 3-D Liver Segmentation using Combined Approach of K-Means and Segmentation Algorithm. Bioinformatics and Medical Engineering. 5(1): 7-14. 2012.

Siddiqui, U. The Optimized K-Means Clustering Algorithm using An Image Segmentation with The Capability of Avoiding The Dead Centre and Trapped Centre at Local Minima. Engineering Application of Artificial Intelligence.13(3): 263-278. 2012.

Vlase, M., Munteanu, D., and Istrate A. An Improvement of K-Means Clustering Algorithm using Various Patent Metadata. International Journal on Computers and Mathematics. 49: 757-763. 2012.

Li, X. Evaluates Clustering in Image Indexing using K-Means Clustering Algorithm and Genetic Algorithm. Journal of Intelligent Information Systems. 23: 5-16. 2012.

Badan Pusat Statistik (BPS). Jawa Tengah dalam Angka. Badan Pusat Statistik: Jawa Tengah. 2014.


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