Implementation of Association Rule Mining Based on Frequent Pattern Growth Algorithms for Sales Recommendations
The level of competition and complexity of sales problems at retail companies, requires each retail company to be able to compete with other companies. One thing that can be done is through making decisions regarding sales that are more appropriate and effective. The amount of transactional data on retail company sales can be extracted useful information. The method that can be used to gather information is through the application of association rule mining. Association Rule Mining is a data mining method that focuses on transaction patterns by extracting associations or relationships of events. The market basket in a computerized retail company is the best way to provide scientific decision support support by determining the relationship between items purchased simultaneously in each transaction. FP-growth algorithm is used to determine the set of datasets that most often appear (frequent itemset) in a group of data. This research resulted in a minimum support value of 0.1% and a minimum value of 60% confidence in the number of rules produced amounted to 116457, a minimum value of 70% confidence in the number of rules produced amounted to 84086, and a minimum value of 80% confidence in the number of rules generated amounted to 48623 from the data processed in a number 22191. The results of this rule can be used for product marketing strategies. The minimum value of support is 0.1% where the greater the minimum value of confidence will result in fewer rules.
 W. A. Triyanto, “Association Rule Mining Untuk Penentuan Rekomendasi Promosi Produk,” J. SIMETRIS, vol. Vol.5, no. No.2, pp. 121–126, 2014.
 D. P. Larasati, M. Nasrun, and U. A. Ahmad, “Analisis Dan Implementasi Algoritma Fp-Growth Pada Aplikasi Smart Untuk Menentukan Market Basket Analysis Pada Usaha Retail ( Studi Kasus : Pt . X ) Analysis and Implementation of Fp-Growth Algorithm in Smart Application To Determine Market Basket Analysi,” Sist. Komput., vol. 2, no. 1, pp. 749–755, 2015.
 D. J. Berndt, M. C. Tremblay, and S. L. Luther, Data mining A Knowledge Discovery Approach. New York, USA: springer.com, 2009.
 M. S. Chen, J. Han, and P. S. Yu, “Data mining: An overview from a database perspective,” IEEE Trans. Knowl. Data Eng., vol. 8, no. 6, pp. 866–883, 1996, doi: 10.1109/69.553155.
 K. and M. . D. Dharmaraajan, “Analysis of FP-Growth and Apriori Algorithms on Pattern Discovery from Weblog Data,” IEEE Int. Conf. Adv. Comput. Appl., pp. 170–174, 2016, doi: 10.1109/ICACA.2016.7887945.
 M. Goel and K. Goel, “FP-Growth Implementation Using Tries for Association Rule Mining,” in Proceedings of Sixth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing, 2017, pp. 21–29, doi: 10.1007/978-981-10-3325-4.
 J. Han and J. Pei, “Mining Frequent Patterns by Pattern Growth: Methodology and Implications,” ACM SIGKDD Explor Newsl., pp. 14–20, 2000.
 Triyanto. Wiwit Agus, Suhartono. Vincent and and Himawan. H, “Analisis Keranjang Pasar Menggunakan K Medoids dan FP Growth.pdf,” J. Pseudocode, vol. 2 Nomor 1, no. September, pp. 129–142, 2014.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
JATIS oleh http://jurnal.mdp.ac.id/index.php/jatisi disebarluaskan di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.