Marketing Strategy Using Frequent Pattern Growth

Nazori Suhandi; Rendra Gustriansyah

doi:10.47709/cnahpc.v3i2.1039

Authors

Nazori Suhandi Universitas Indo Global Mandiri
Rendra Gustriansyah Universitas Indo Global Mandiri

DOI:

10.47709/cnahpc.v3i2.1039

Keywords:

Association Rules, Data mining, FP-Growth, Marketing Strategy, Printing

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Abstract

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.

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References

Abdullah, A. (2018). Rekomendasi Paket Produk Guna Meningkatkan Penjualan dengan Metode FP-Growth. Khazanah Informatika: Jurnal Ilmu Komputer Dan Informatika, 4(1), 21. https://doi.org/10.23917/khif.v4i1.5794

Ariestya, W. W., Supriyatin, W., & Astuti, I. (2019). Marketing Strategy for The Determination of Staple Consumer Products Using FP-Growth and Apriori Algorithm. Jurnal Ilmiah Ekonomi Bisnis, 24(3), 225–235. https://doi.org/10.35760/eb.2019.v24i3.2229

Destrilia, Primartha, R., Sukemi, & Wijaya, A. (2020). Online Retail Marketing Recommendation System Based on Generalized Sequential Pattern Algorithm and FP-Growth Algorithm. In Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Paris, France: Atlantis Press. https://doi.org/10.2991/aisr.k.200424.053

Gustriansyah, R., Suhandi, N., & Antony, F. (2019). The Design of UML-Based Sales Forecasting Application. International Journal of Recent Technology and Engineering, 7(6), 1507–1511.

Gustriansyah, Rendra, Sensuse, D. I., & Ramadhan, A. (2015). Decision support system for inventory management in pharmacy using fuzzy analytic hierarchy process and sequential pattern analysis approach. In 2015 3rd International Conference on New Media (CONMEDIA) (pp. 1–6). IEEE. https://doi.org/10.1109/CONMEDIA.2015.7449153

Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data - SIGMOD ’00 (pp. 1–12). New York, New York, USA: ACM Press. https://doi.org/10.1145/342009.335372

Hossain, M., Sattar, A. H. M. S., & Paul, M. K. (2019). Market Basket Analysis Using Apriori and FP Growth Algorithm. In 2019 22nd International Conference on Computer and Information Technology (ICCIT) (pp. 1–6). IEEE. https://doi.org/10.1109/ICCIT48885.2019.9038197

Lubis, A. E., & Hasugian, P. M. (2020). Implementation of Data Mining on Suzuki Motorcycle Sales in Gemilang Motor Prosperous with Apriori Algorithm Method. Journal of Computer Networks, Architecture and High Performance Computing, 2(1), 23–29. https://doi.org/10.47709/cnapc.v2i1.353

Meida, A., Rini, D. P., & Sukemi, S. (2019). Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 7(4). https://doi.org/10.11591/ijeei.v7i4.1362

Novita, R., Mustakim, & Salisah, F. N. (2021). Determination of the relationship pattern of association topic on Al-Qur’an using FP-Growth Algorithms. IOP Conference Series: Materials Science and Engineering, 1088(1), 012020. https://doi.org/10.1088/1757-899X/1088/1/012020

Putro, A. N. S., & Gunawan, R. I. (2019). Implementasi Algoritma FP-Growth untuk Strategi Pemasaran Ritel Hidroponik (Studi Kasus?: PT. HAB). Jurnal Buana Informatika, 10(1), 11. https://doi.org/10.24002/jbi.v10i1.1746

Setiawan, H., Sumitro, A. A. A., & Gustriansyah, R. (2019). The change data capture and the web application messaging protocol on the real time dashboard. International Journal of Engineering and Advanced Technology, 8(4).

Siregar, A. K., Kusuma, B. A., Kuncoro, A. P., & Suliswaningsih. (2018). Perbandingan Algoritme FP-Growth dan Eclat untuk Analisis Pola Pembelian Konsumen pada Toko “X.” In Conference on Information Technology, Information System and Electrical Engineering (CITISEE) (pp. 125–128). Purwokerto: Universitas AMIKOM Purwokerto.

Srikant, R., & Agrawal, R. (1996). Mining Sequential Patterns: Generalizations and Performance Improvements. In 5th International Conference on Extending Database Technology Avignon (pp. 1–17). France.

Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390. https://doi.org/10.1109/69.846291