Identifikasi Strategi Penjualan dengan Pendekatan Asosiasi FP-Growth Pada Perusahaan Ritel Berkah

Foezi Arisandi SJ

doi:10.35957/jatisi.v10i1.3175

Foezi Arisandi SJ STIMIK LIKMI BANDUNG

DOI: https://doi.org/10.35957/jatisi.v10i1.3175

Keywords: data mining, association, fp-growth algorithm, retail

Abstract

In the face of global trends, today the world is faced with "global uncertainty" where there has been an irreversible trend in the business world and the market that has changed the way a company does business to become more towards digital business, therefore decision-making by a company executive is based on data, facts, and knowledge quickly and accurately becomes a major requirement to increase competitiveness in various business fields, therefore we take Berkah shop which is a retail company that sells multiple needs for household appliances, cafes, restaurants, and catering located in Sukabumi City for research. This study will aim to find data, information, and knowledge to determine the right sales strategy, including product bundling and product display, for, now there are still obstacles in determining the strategy above because there has not been an analysis of existing sales data so it is still difficult to determine the product. anything that is often purchased by customers in one sales receipt and what products are related to each other or form association rules to be installed in product displays. So we need a method that usable to deal with existing problems, in this study the method used is the Data Mining Association analysis method by applying the Frequent Pattern Growth (FP-Growth) algorithm in the transaction data analysis process for one year (June 01, 2021, until May 31, 2022), the data for the period has several sales transactions of 13734 with a total record of 49360. To help achieve the optimal finish, the RapidMiner application software is used. The results of the application of association rules in this study resulted in the highest 6 association rules with a minimum support value of 0.001 and a minimum confidence value of 0.01 and resulted in the lowest 42 association rules with a minimum support value of 0.001 and a minimum confidence value of 0.5. The association rules found can be used as material for consideration in making effective and accurate product bundling and product display sales strategy decisions so that in particular the company can increase sales and in general improve competitiveness

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