Eksplorasi Pola Pembelian Di Kedai Bunsigjib Menggunakan Teknik FP-Growth
Keywords:
Data, Asosiasi, Algoritma FP-Growth, Pola BelanjaAbstract
Kedai Bunsigjib is one of the many shops in the city of Cirebon that sells Korean food and drinks. Every day, there are sales transactions at Kedai Bunsigjib. Sometimes consumers don't just buy one food or drink, but two or more foods or drinks in one transaction. The recording of transactions is still limited to documentation and has not been used, only allowed to pile up by Kedai Bunsigjib. Transaction data is also related to spending patterns but has not been utilized by Kedai Bunsigjib. Whereas shopping patterns can be used to increase sales and speed up the service process at Kedai Bunsigjib. The purpose of this study was to determine the pattern (rules) of consumer shopping associations at Kedai Bunsigjib using the FP-Growth Algorithm. The association method includes the FP-Growth Algorithm. The data used is data obtained from the Bunsigjib shop. The dataset is a history of food and beverage orders. Furthermore, the association of the dataset is carried out. Then the pattern of association rules is evaluated using the Lift Ratio. So the results obtained are that there are 2 valid itemsets based on the accuracy of the lift ratio which states that the itemset is valid if > 1, which is a high possibility if consumers will buy Lemon Tea first, they will buy Regular Tteokpeokki with 90% support and 10.9% confidence , and if consumers want to buy Regular Tteokpeokki first then buy Lemon Tea with 90% Support and 13.1% Confidence.
References
Han, J., Pei, J., & Kamber, M. (2022). Data Mining: Concepts and Techniques (4th ed.). Elsevier.
Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.
Tan, P. N., Steinbach, M., & Kumar, V. (2018). Introduction to Data Mining (2nd ed.). Pearson.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data Mining: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.
Widyanto, A., & Sutrisno, R. (2021). Analisis Market Basket Menggunakan Algoritma FP-Growth pada Data Transaksi Ritel. Jurnal Sistem Informasi dan Komputer, 9(2), 102–109.
Nurhidayat, R., & Oktaviani, S. (2020). Penerapan Algoritma FP-Growth dalam Menentukan Pola Pembelian Konsumen pada Usaha Kuliner. Jurnal Ilmiah Teknologi dan Informatika, 11(1), 55–63.
Kusuma, D., & Permana, R. A. (2022). Penerapan Association Rule Mining untuk Analisis Penjualan Produk Makanan. Jurnal Teknologi Informasi dan Komputer, 7(1), 75–84.
Handayani, L., & Prasetyo, A. (2020). Implementasi Algoritma FP-Growth dalam Menganalisis Pola Pembelian Produk di Minimarket. Jurnal Informatika dan Komputasi, 11(2), 88–95.
Sembiring, R., & Siregar, A. (2023). Pengembangan Strategi Pemasaran Berdasarkan Pola Pembelian Konsumen Menggunakan FP-Growth. Jurnal Sistem Cerdas, 6(1), 43–51.
Hermawan, D., & Utomo, W. (2019). Analisis Association Rule untuk Pola Belanja Pelanggan Restoran. Jurnal Riset Sistem Informasi dan Teknologi Informasi, 8(2), 33–40











