System for Determining Public Health Level Using the Agglomerative Hierarchical Clustering Method

Suhirman Suhirman, Hero Wintolo

Submitted : 2019-02-27, Published : 2019-03-22.


Regions having higher level of welfare do not always have better indicator values than other regions having lower level of welfare. The problem is the lack of information related to the indicator values needed to determine the health level. Therefore, clustering using health data becomes necessary. Data were clustered to see the maximum or the minimum level of similarity. The clustered data were based on the similarity of four morality indicator values of the regional health level. Morality indicator values used in this research are infant mortality rate, child mortality rate, maternal mortality rate, and rough birth rate. The method used is Agglomerative Hierarchical Clustering (AHC) - Complete Linkage. Data were calculated using Euclidean Distance Equation, then Complete Linkage. Four clustered data were grouped into two clusters, healthy and/or unhealthy. The result, combining from all clusters into two large clusters to see healthy and unhealthy results.


Health Level, Health Indicators, Agglomerative Hierarchical Clustering, Cluster


Mesakar, S. S., & Chaudhari, M. S. (2012). Review Paper On Data Clustering Of Categorical Data. International Journal of Engineering Research & Technology (IJERT), 1(10), 1-18.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.

Jiang, D., Tang, C., & Zhang, A. (2004). Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge & Data Engineering, (11), 1370-1386.

Giannotti, F., Gozzi, C., & Manco, G. (2002, August). Clustering transactional data. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 175-187). Springer, Berlin, Heidelberg.

Mathieu, R. G., & Gibson, J. E. (1993). A methodology for large-scale R&D planning based on cluster analysis. IEEE Transactions on Engineering Management, 40(3), 283-292.

Haimov, S., Michalev, M., Savchenko, A., & Yordanov, O. I. (1989). Classification of radar signatures by autoregressive model fitting and cluster analysis. IEEE Transactions on Geoscience and Remote Sensing, 27(5), 606-610.

Yanto, I. T. R., Herawan, T., & Deris, M. M. (2011). Data clustering using variable precision rough set. Intelligent Data Analysis, 15(4), 465-482.

Yanto, I. T. R., Vitasari, P., Herawan, T., & Deris, M. M. (2012). Applying variable precision rough set model for clustering student suffering study’s anxiety. Expert Systems with Applications, 39(1), 452-459.

Herawan, T. (2012). Rough clustering for cancer datasets. In International Journal of Modern Physics: Conference Series (Vol. 9, pp. 240-258). World Scientific Publishing Company.

Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. The computer journal, 41(8), 578-588.

Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.

Banfield, J. D., & Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 803-821.

Prasetyo, E. (2012). Data Mining konsep dan Aplikasi menggunakan MATLAB. Yogyakarta: Andi.

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