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A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Hartono, Devi Fitrianah

Hartono and Devi Fitrianah. A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm. International Journal of Computer Applications 177(38):37-44, February 2020. BibTeX

	author = {Hartono and Devi Fitrianah},
	title = {A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2020},
	volume = {177},
	number = {38},
	month = {Feb},
	year = {2020},
	issn = {0975-8887},
	pages = {37-44},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2020919889},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This study aims to find interesting patterns on the database transaction so that it can be used as a recommendation sales promotion and inventory product. Companies have difficulty finding interesting transaction patterns in large databases, so it will be difficult to determine the right product promotions and inventory. To resolve these problems are to use data mining techniques with association rule. In previous studies, most studies adopt Apriori algorithm to analyze the association rules. In this study, the data mining technique used is the association rules algorithm FP-Growth. In the FP-Growth algorithm did generate candidates as in Apriori algorithm and using a development concept Tree in frequent itemset search so that it requires faster than Apriori. Some of the analyzes produced in this study are higher minimum support values and minimum trust used will result in fewer items and association rules. Association rule in this study has a lift ratio value of more than 1.00, meaning that item K and L are actually bought together. The higher the lift ratio produced shows the stronger the association rules are formed. The results of this study are the minimum confidence of 97.63%, the maximum trust is 99.37% and the lift ratio is 1,00013798. These results can be used as recommendations for optimizing product promotions and inventory.


  1. A. Abdullah, “Rekomendasi Paket Produk Guna Meningkatkan Penjualan Dengan Metode FP-Growth,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 1, p. 21, 2018.
  2. D. Sukma and D. Fitrianah, “Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm,” Int. J. Comput. Appl., vol. 181, no. 15, pp. 7–15, 2018.
  3. N. Arincy and I. S. Sitanggang, “Association rules mining on forest fires data using FP-Growth and ECLAT algorithm,” Proc. - 2015 3rd Int. Conf. Adapt. Intell. Agroindustry, ICAIA 2015, pp. 274–277, 2016.
  4. F. Fitriyani, “Implementasi Algoritma Fp-Growth Menggunakan Association Rule Pada Market Basket Analysis,” J. Inform., vol. 2, no. 1, 2016.
  5. A. Wahana, D. S. Maylawati, M. Irfan, and H. Effendy, “Supply chain management using fp-growth algorithm for medicine distribution,” J. Phys. Conf. Ser., vol. 978, no. 1, 2018.
  6. A. N. S. Putro and R. I. Gunawan, “Implementasi Algoritma FP-Growth Untuk Strategi Pemasaran Ritel Hidroponik (Studi Kasus : PT. HAB),” J. Buana Inform., vol. 10, no. 1, p. 11, 2019.
  7. M. I. Ghozali, R. Z. Ehwan, and W. H. Sugiharto, “Analisa Pola Belanja Menggunakan Algoritma Fp Growth, Self Organizing Map (Som) Dan K Medoids,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 8, no. 1, pp. 317–326, 2017.
  8. M. Kaur and S. Kang, “Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining,” Procedia Comput. Sci., vol. 85, no. Cms, pp. 78–85, 2016.
  9. N. Hashimoto, S. Ozawa, T. Ban, J. Nakazato, and J. Shimamura, “A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning,” Procedia Comput. Sci., vol. 144, pp. 118–123, 2018.
  10. W. Feng, Q. Zhu, J. Zhuang, and S. Yu, “An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth,” Cluster Comput., pp. 1–12, 2018.
  11. K. Sumangkut, A. S. M. Lumenta, and V. Tulenan, “Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth,” J. Tek. Inform., vol. 8, no. 1, 2016.
  12. Midhunchakkaravarthy, Divya, D. Bhattacharyya, and T. H. Kim, “Evaluation of product usability using improved FP-Growth frequent itemset algorithm and DSLC – FOA algorithm for alleviating feature fatigue,” Int. J. Adv. Sci. Technol., vol. 117, pp. 163–180, 2018.
  13. W. T. Lin and C. P. Chu, “A fast and parallel algorithm for frequent pattern mining from big data in many-task environments,” Int. J. High Perform. Comput. Netw., vol. 10, no. 3, pp. 157–167, 2017.
  14. M. Yin, W. Wang, Y. Liu, and D. Jiang, “An improvement of FP-Growth association rule mining algorithm based on adjacency table,” MATEC Web Conf., vol. 189, pp. 0–6, 2018.
  15. R. Anggrainingsih, N. R. Khoirudin, and H. Setiadi, “Discovering drugs combination pattern using fp-growth algorithm,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 4, no. September, pp. 735–738, 2017.
  16. A. -, F. Marisa, and D. Purnomo, “Penerapan Algoritma Apriori Terhadap Data Penjualan di Toko Gudang BM,” Penerapan Algoritm. Apriori Terhadap Data Penjualan di Toko Gudang BM, vol. 1, no. 1, pp. 1–5, 2016.
  17. Y. Gao, Q. Zhang, L. Chen, K. Wang, N. Chen, and H. Liu, “Research on application of FP-tree based association rule mining on test sequence in train control system,” Res. Appl. FP_tree based Assoc. rule Min. test Seq. train Control Syst., pp. 9993–9997, 2017.
  18. H. Herasmus, “Analisa Customer Service System Menggunakan Metode Data Mining Dengan Algoritma Fp-Growth (Studi Kasus Di Pt Batamindo Investment Cakrawala),” J. Tek. Ibnu Sina JT-IBSI, vol. 2, no. 2, pp. 31–43, 2017.
  19. K. Dharmarajan and M. A. Dorairangaswamy, “Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data,” 2016 IEEE Int. Conf. Adv. Comput. Appl. ICACA 2016, pp. 170–174, 2016.
  20. A. R. Riszky and M. Sadikin, “Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan,” J. Teknol. dan Sist. Komput., vol. 7, no. 3, p. 103, 2019.
  21. Y. Jumiati and N. Bahtiar, “Pengembangan Sistem Informasi Data KB dan Analisis Pola Pemilihan Metode Kontrasepsi Menggunakan Algoritma Sql-Based Fp-Growth,” PERFORMA Media Ilm. Tek. Ind., vol. 15, no. 1, pp. 70–76, 2016.


Promotion, Knowledge Discovery Database Data Mining, Association Rule, Fp-Growth, frequent itemset.