Apriori Algorithm to Predict Availability of Beauty Products

Maria Hikmah Hasibuan; M.Fakhriza

doi:10.47709/cnahpc.v6i3.4259

Authors

Maria Hikmah Hasibuan Computer Science Department, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
M.Fakhriza Computer Science Department, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia

DOI:

10.47709/cnahpc.v6i3.4259

Keywords:

Data Mining, Apriori Algorithm, Beauty Products, Inventory Management, Consumer Behavior

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Abstract

This study introduces the Apriori algorithm in beauty product availability prediction system as a solution to enhance stock prediction accuracy and mitigate inventory risks in the beauty industry. By applying data mining technology, specifically the Apriori algorithm, Kazana Kosmetik aims to gain insights into consumer purchasing patterns to optimize operations. The research analyzes transaction data to identify key buying patterns and improve stock management strategies. The results reveal seven main purchasing patterns with an average confidence value of 0.414, offering valuable guidance for Kazana Kosmetik in inventory control and marketing tactics. By leveraging data mining techniques, companies like Kazana Kosmetik can streamline sales strategies and enhance customer satisfaction. This research underscores the effectiveness of the Apriori algorithm in predicting beauty product availability and its potential to revolutionize operational efficiency in the cosmetics market.

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