Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket

Andy Hermawan; Bayu Wicaksono; Tigfhar Ahmadjayadi; Bagas Surya Prakasa; Jasico Dacomoro Aruan

doi:10.62383/algoritma.v2i5.137

Authors

Andy Hermawan Universitas Indraprasta PGRI
Bayu Wicaksono Purwadhika Digital Technology School
Tigfhar Ahmadjayadi Purwadhika Digital Technology School
Bagas Surya Prakasa Purwadhika Digital Technology School
Jasico Dacomoro Aruan Purwadhika Digital Technology School

DOI:

https://doi.org/10.62383/algoritma.v2i5.137

Keywords:

Market Basket Analysis, Association Rule Learning, Apriori Algorithm

Abstract

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.

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