在阿尔法玛特中心公园使用 apriori 算法对销售交易进行数据挖掘的应用

Martinus Zega, Rahmat Fauzi
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引用次数: 1

摘要

随着经济的逐年发展,零售业是几个行业中发展比较好的行业之一,其中一个是PT Sumber Alfaria Trijaya Tbk。或者俗称阿尔法玛。在Alfamart,市场营销活动的重点是购买和销售食品和非食品商品,在每日总销售额中有足够多的交易,公司需要分析工具来为公司提供有用的信息。因为Alfamart是一家大型的零售公司,所以在日常生活中经常会遇到几个问题,即确定不太具有战略意义和容易被顾客注意到的商品的布局,不知道消费者最经常同时购买的产品,以及在一个积压仓库中有几种类型的商品。因此,本研究使用Apriori算法对Alfamart的销售额进行数据挖掘,并使用Rapidminer软件进行测试,以产生最高的关联规则。本研究的结果是发现了10条关联规则,最低支持度为40%,置信度为70%,有望为商业行为者改善销售策略提供建议。 & # x0D;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PENERAPAN DATA MINING PADA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI DI ALFAMART CENTRE PARK
Along with the development of the economy from year to year, the retail industry is one of several industries that is experiencing quite good development, one of which is PT Sumber Alfaria Trijaya Tbk. or commonly known as Alfamart. Marketing activities at Alfamart that focus on buying and selling food and non-food goods with a large enough number of transactions in total daily sales, a company needs analytical tools to provide useful information for the company. Because Alfamart is a large-scale retail company, there are several problems that are often encountered daily, namely determining the layout of goods that are less strategic and easily noticed by customers, not knowing which products are most often purchased simultaneously by consumers and several types of goods in an overstock warehouse. Therefore, research was carried out with the implementation of data mining on sales at Alfamart using the Apriori algorithm and testing using the Rapidminer software to produce the highest association rules. The results of this study are the discovery of 10 association rules with a minimum support of 40% and 70% confidence which are expected to be recommendations for business actors to improve sales strategy.
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