Management System For Determining Promotional Strategies Cut Nya' Dien Vocational School With Data Mining Using K-Means Clustering Algorithm
Keywords:
Promotion strategy, data mining, clustering, k-means, Cut Nya' Dien Vocational SchoolAbstract
The development of the number of new students at Cut Nya' Dien Vocational School experiences ups and downs every year. The reason is because the data was not processed appropriately based on historical data. The aim of this research is to determine the right promotional strategy based on the results of the groupings formed. One solution that can be used is data mining techniques using the Kmeans Clustering algorithm. Clustering is a method for searching and grouping data that has similar characteristics between one data and another. K-Means is a non-hierarchical data clustering method that attempts to divide data into one or more clusters. The results of this research will form 3 clusters and are expected to be one of the basic considerations in making promotion strategy decisions based on the clusters formed by the school principal.
References
Asriningtias, Y., & Mardhiyah, R. (2014). Application of data mining to display information on student graduation levels. Journal of Informatics, 8(1).
Elvira, et al. (2015). Analysis of graduate data using data mining to support Lancang Kuning University's promotion strategy. Journal of Information and Communication Technology Digital Zone, 6(2).
Jogiyanto. (2010). Information technology systems. Yogyakarta: Andi.
Kotler, P., & Armstrong, G. (2001). Marketing principles (8th ed., Vol. 1). Jakarta: Erlangga.
Kusrini, & Taufiq, L. E. (2009). Data mining algorithms. Yogyakarta: Andi.
Lucas, H. (2012). Design analysis and implementation of information systems. Jakarta: Erlangga.
McLeod, R., Jr., & Schell, G. P. (2008). Management information systems. Jakarta: Salemba Empat.
Mulyanto, A. (2009). Information systems concepts & applications. Yogyakarta: Pustaka Pelajar.
Ong, J. O. (2013). Implementation of the K-means clustering algorithm to determine President University's marketing strategy. Industrial Engineering Scientific Journal, 12(1).
Prasetyo, E. (2012). Data mining concepts and applications using Matlab. Yogyakarta: Andi.
Santosa, B. (2007). Data mining: Techniques for using data for business needs. Yogyakarta: Graha Ilmu.
Santoso, S. (2010). Multivariate statistics. Jakarta: Elex Media Komputindo.
Turban, E., et al. (2005). Introduction to data mining. Pearson.
Yakub. (2012). Introduction to information systems. Yogyakarta: Graha Ilmu.
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