The Analysis and Implementation of Algorithm of Frequent Pattern – Growth to Support the Promotion Strategy in Victory University Sorong

International Journal of Computer Science and Engineering

© 2017 by SSRG - IJCSE Journal

Volume 4 Issue 10

Year of Publication : 2017

Authors : Iriene Surya Rajagukguk, Sri Yulianto Joko Prasetyo, Irwan Sembiring

10.14445/23488387/IJCSE-V4I10P106

How to Cite?

Iriene Surya Rajagukguk, Sri Yulianto Joko Prasetyo, Irwan Sembiring, "The Analysis and Implementation of Algorithm of Frequent Pattern – Growth to Support the Promotion Strategy in Victory University Sorong," SSRG International Journal of Computer Science and Engineering , vol. 4,  no. 10, pp. 24-31, 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I10P106

Abstract:

Nowadays, the increasing number of campuses is growing, which makes want to get a better promotion strategy. As for how to find the right promotional strategy will be able to reduce the cost of promotion and achieve the right promotional goals. One way that can be done for the determination of a promotion strategy is to use data mining techniques. The technique used in this case is FP-Growth Algorithm is an alternative algorithm that can be used to determine the most frequent item set in a data set. Research is done by observing some research variables that are often considered by universities, especially the marketing section in determining the promotional target of the last education, the origin of the region, the choice of major and promotion strategy. The result of this research is a suitable promotion strategy to be applied at Victory University Sorong namely advertising, personal selling and publicity. And the most influential is advertising, this can be seen from the amount of the advertising value of 35 transaction from the total sample number of 43 transaction with a significant level 81.40 % of the other dimensions. Personal selling 6 transactions, the effect is 13.95 % a publicity as much as 2 transactions or 4.65 % of the other dimensions.

Keywords:

Data mining, association rules, frequent item set, FP-growth algorithm.

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