IMPLEMENTATION OF MARKET BASKET ANALYSIS WITH APRIORI ALGORITHM IN MINIMARKET

Abdul Hafiidh Priyanto; Amalia Beladinna Arifa

doi:10.20884/1.jutif.2022.3.5.606

Abdul Hafiidh Priyanto Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto, Indonesia
Amalia Beladinna Arifa Teknik Informatika, Fakultas Informatika, Institut Teknologi Telkom Purwokerto, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.5.606

Keywords: Apriori Algorithm, Flask, Market Basket Analysis, Transaction Data

Abstract

The rapid growth of the retail business has an impact on increasing the economic growth of the community. The retail business has high profit potential in areas that have a large population such as Indonesia. A retail business that is popular among the public is a modern market retail business or convenience store. With the rapid growth, it gives a tendency between convenience stores to compete. By designing a marketing strategy is one of the efforts to win the competition in supermarkets. Management needs to understand the purchase behavior made by customers, this action is useful to find out the products that customers are popularly buying. Association algorithm is a form of algorithm in the field of data mining that serves to provide correlation between one item and another. there are several popular algorithms in applying association algorithms one of which is the a priori algorithm created by Agrawal and Srikant in 1994. To support the understanding of customer purchase patterns, it is necessary to implement market basket analysis that has the ability to recognize pattern patterns from transaction data in a convenience store. Performance in market basket analysis also needs to be tested to handle a lot of transaction data, considering that the recording of sales transaction data continues to run over time. The implementation carried out using flask is one of the implementations that is relevant to technological developments, this implementation results in a relatively short data speed with the factor that the magnitude of transaction data is middle to lower, which is 14,963 transaction data.

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