Implementasi Algoritma Apriori sebagai Strategi Pemasaran Produk Harian

Erlin Elisa; Tukino Tukino

Authors

Erlin Elisa Universitas Putera Batam
Tukino Tukino Universitas Putera Batam

Keywords:

Data Mining, Apriori Algorithms,, Market Basket Analysis

Abstract

This minimarket is located close to people's homes, this certainly affects the level of sales, with the existence of sales activities every day, sales transaction data will continue to grow, causing data storage to get bigger. Sales transaction data is only used as an archive without being used properly. This study aims to apply the Market Basket Analysis method using a priori algorithm in predicting sales promotion itemsets in minimarkets. A priori is a very well-known algorithm for finding high-frequency patterns. High-frequency patterns are patterns of items in a database that have frequency or support above a certain threshold called minimum support. The results of this study found that the highest support and confidence values were beverage and snack items with a support value of 33.3% and confidence of 80%. Using a priori algorithms can help to develop a marketing strategy.

References

Subarsono, D. (2014). Perbedaan Pelayanan Pada Ritel Tradisional Dengan Ritel Modern Di Kota Cirebon ., 2(2).

Nursikuwagus, T. Hartono, “Implementasi Algoritma Apriori Untuk Analisis Penjualan Dengan Berbasis Web,” Jurnal SIMETRIS, vol. 7, no. 2, hal. 701-706, 2016.

Agrawal R., T. Imielinski, and A.Swami. 1993. Special Issue on Learning and Discovery in Knowledge Based Databases. Database Mining : A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, 914-925.

S.F. Rodiyansyah, “Algoritma Apriori untuk Analisis Keranjang Belanja pada Data Transaksi Penjualan,” Infotech Journal, vol. 1, no. 2, 2016.

Agrawal R. and R. Srikant. 1994. Fast Algorithms for Mining Association Rules in Large Databases. Research Report RJ 9839. San Jose, CA: IBM Almaden Research Center

Ambarwaty, Retno. 1997. Sistem Pendukung Keputusan Pemasaran Jasa Telepon Dengan Segmentasi Pelanggan Psikografis. Studi Kasus di Kandatel Jakarta Barat. Bandung: Sekolah Tinggi Teknologi Telkom.

Direct Marketing Association. 1992. Managing Database Marketing Technology for Sucess.

Elisa, E. (2018). Market Basket Analysis Pada Mini Market Ayu Dengan Algoritma Apriori. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2(2), 472-478.

Date, C.J. 1994. An Introduction to Database System. New York: Addison Wesley.