The Application Of K-Means Algorithm For LQ45 Index on Indonesia Stock Exchange


  • A. Raharto Condrobimo Bina Nusantara University
  • Albert V. Dian Sano Bina Nusantara University
  • Hendro Nindito Bina Nusantara University



blue chip stock, data mining, k-means, clustering


The objective of this study is to apply cluster analysis or also known as clustering on stocks data listed in LQ45 index at Indonesia Stock Exchange. The problem is that traders need a tool to speed up decision-making process in buying, selling and holding their stocks.The method used in this cluster analysis is k-means algorithm. The data used in this study were taken from Indonesia Stock Exchange. Cluster analysis in this study took data’s characteristics such as stocks volume and value. Results of cluster analysis were presented in the form of grouping of clusters’ members visually. Therefore, this cluster analysis in this study could be used to identify more quickly and efficiently about the members of each cluster of LQ45 index. The results of such identification can be used by beginner-level investors who have started interest in stock investment to help make decision on stocks trading.


Plum Analytics


Athanasios, V., & Antonios, A. (2012). Stock market development and economic growth an empirical analysis. American Journal of Economic and Business Administration, 4, 135-143.

Gothai, E., & Balasubramanie, P. (2012). An efficient way for clustering using alternative decision tree. American Journal of Applied Science, 9, 531-534.

Han, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques (4th ed.). San Francisco: Morgan Kaufmann Publishers.

Hossain, J., Fazlida Mohd Sani, N., Mustapha, A., & Affendey, L.S.(2013). Using feature selection as accuracy benchmarking in clinical data mining. Journal of Computer Science, 9,883-888. IDX. (n.d.). Bagi Perusahaan. Retrieved from

Kumar, S.P., & Ramaswami, K.S. (2011). Fuzzy modeled k-cluster quality mining of hidden knowledge for decision support. Journal of Computer Science, 7, 1652-1658. LQ45. (2015, February 5). Retrieved from

Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance. International Journal of Computer Science and Information Security (IJCSIS), 7(1). Retrieved on August 3, 2015


Rui, X., Donald, W. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, 16(3), 645 – 678.

Silwattananusarn, T., & Tuamsuk, K. (2012). Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012. International Journal of Data Mining

& Knowledge Management Process (IJDKP), 2(5). Retrieved on August 3, 2015 from

Tajunisha, S. (2010). Performance analysis of k-means with different initialization methods for high

dimensional data. International Journal of Artificial Intelligence & Applications (IJAIA), 1(4), 44-52. Retrieved on August 3, 2015 from

Tayal, M.A., & Raghuwanshi, M.M. (2011). Review on Various Clustering Methods for the Image Data. Journal of Emerging Trends in Computing and Information Sciences, 2 Special Issue.

Wang, H., & Song, M. (December, 2011). Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming. The R Journal, 3(2), 29-32.






Abstract 434  .
PDF downloaded 260  .