IMPLEMENTATION OF APRIORI ALGORITHM IN DETERMINING THE LEVEL OF PRINTING NEEDS

##plugins.themes.academic_pro.article.main##

R. Mahdalena Simanjorang

Abstract

Competition in the business world, especially in the increasingly difficult printing world, requires developers to find strategies to increase orders for printed products ordered. An increasing number of order data every day can be used to develop marketing strategies if processed correctly. A priori algorithms include the type of association rules in data mining. One of the stages of association analysis that attracts many researchers to produce efficient algorithms is the analysis of high-frequency patterns (frequent pattern mining). The importance of an association can be known by two benchmarks, namely: support and confidence. Support (support value) is the percentage of the combination of these items in the database, while confidence (certainty value) is the strength of the relationship between items in association rules.

##plugins.themes.academic_pro.article.details##

How to Cite
R. Mahdalena Simanjorang. (2020). IMPLEMENTATION OF APRIORI ALGORITHM IN DETERMINING THE LEVEL OF PRINTING NEEDS. INFOKUM, 8(2, Juni), 43-48. Retrieved from http://infor.seaninstitute.org/index.php/infokum/article/view/16

References

[1] S. Džeroski, “Data Mining,” in Encyclopedia of Ecology, Five-Volume Set, 2008.
[2] X. Wu, X. Zhu, G. Q. Wu, and W. Ding, “Data mining with big data,” IEEE Trans. Knowl. Data Eng., 2014.
[3] X. Wu et al., “Top 10 algorithms in data mining,” Knowl. Inf. Syst., 2008.
[4] R. Sowmya and K. R. Suneetha, “Data Mining with Big Data,” in Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, 2017.
[5] C. Borgelt and C. Borgelt, “Efficient Implementations of Apriori and Eclat,” PROC. 1ST IEEE ICDM Work. Freq. ITEM SET Min. IMPLEMENTATIONS (FIMI 2003, MELBOURNE, FL). CEUR Work. Proc. 90, 2003.
[6] A. Bhandari, A. Gupta, and D. Das, “Improvised apriori algorithm using frequent pattern tree for real time applications in data mining,” in Procedia Computer Science, 2015.
[7] D. S. Kusumo, M. A. Bijaksana, and D. Darmantoro, “DATA MINING DENGAN ALGORITMA APRIORI PADA RDBMS ORACLE,” TEKTRIKA - J. Penelit. dan Pengemb. Telekomun. Kendali, Komputer, Elektr. dan Elektron., 2016.
[8] A. Nursikuwagus and T. Hartono, “IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISIS PENJUALAN DENGAN BERBASIS WEB,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., 2016.
[9] D. K. Pane, “Implementasi Data Mining Pada Penjualan Produk Elektronik Dengan Algoritma Apriori ( Studi Kasus : Kreditplus ),” Pelita Inform. Budi Darma, 2013.
[10] H. Toivonen, “Apriori Algorithm,” in Encyclopedia of Machine Learning and Data Mining, 2017.
[11] J. Nahar, T. Imam, K. S. Tickle, and Y. P. P. Chen, “Association rule mining to detect factors which contribute to heart disease in males and females,” Expert Syst. Appl., 2013.