Implementasi Algoritma Apriori dalam Meningkatkan Strategi Penjualan pada Toko Miring

Autor: Elly Mufida, Eva Rahmawati, Hylenarti Hertyana, Annisa Desianty
Rok vydání: 2022
Předmět:
Zdroj: MEANS (Media Informasi Analisa dan Sistem). :158-163
ISSN: 2599-3089
2548-6985
DOI: 10.54367/means.v6i2.1524
Popis: Toko Miring is one of the stores that sell various daily completeness needs such as food, drinks, household appliances, and others placed on shelves or storefronts. Each sales transaction data is recorded in a database system through the cashier application. According to Rahmat Fathi, supervisor at Toko Miring, at the beginning of 2020 precisely in March, The Tilt store experienced a decrease in the number of sales transactions compared to the previous year, this is because the buying and selling process in this Tilt store has many problems, one of which is the storage of sales data is still in writing. This causes buyers to have difficulty finding the desired product because the preparation of the product is not separated by type. From the problems that occurred, researchers together with Toko Miring proposed to rearrange a sales strategy to increase sales at Tilt Stores. In devising sales strategies, researchers utilize sales transaction data in previous years to be reprocessed using data mining techniques. Based on the description above, the researcher will analyze sales transaction data using data mining techniques by implementing a priori algorithms with a support value and confidence value of 50% and implementing it into the python programming language. The result of this study is that researchers managed to get sales patterns that can improve sales strategies that produce information that is useful for related parties in making sales strategy decisions such as sales packages for promos, recommending products to customers and maintaining product availability in order to increase sales intensity in some stores such as in Tilt Stores.
Databáze: OpenAIRE
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