ANALISA POLA PEMBELIAN KONSUMEN MENGGUNAKAN DATA MINING DENGAN ALGORITMA APRIORI (STUDI KASUS: EDUKITS BATAM CENTRE)

Elnas Lowensky, Erlin Elisa
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Abstract

The initial research purposes is to do the analyzing of data transaction for one of the stationary stores in Batam city, namely edukits Batam Centre, because the store is a well-known business and of course many consumers are interested, of course, a lot of data has been collected so far. From transactions that have occurred so far, these data are only stored in the form of archive files without being processed or used for future business progress. To analyze this research, the a priori algorithm association rule method was used, by analysing support value and as well as the confidence value that were set so that the results of this study obtained the support value for the Printing Paper category 71%, Accessories 74%, 77%, Other Equipment 88%, Glue and Adhesive 72%. By using the Apriori algorithm, 2 rules are obtained, namely the antecedent value of stationery, the consequent accessories resulting in a support value of 65% and confidence of 84.4%. Then the second rule is the antecedent of accessories, the consequent of writing instruments with a support value of 65% and a confidence value of 88.4%.
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利用挖掘数据与杏算法进行消费者购买模式分析(案例研究:EDUKITS BATAM CENTRE)
最初的研究目的是对巴淡市的一家文具店进行数据交易分析,即edukits Batam Centre,因为这家店是一家知名企业,当然很多消费者都感兴趣,当然到目前为止已经收集了很多数据。对于迄今为止发生的事务,这些数据仅以归档文件的形式存储,而不进行处理或用于未来的业务进展。为了分析本研究,采用先验算法关联规则法,通过对所设置的支持值和置信度进行分析,得出本研究的结果为:印刷纸类支持值为71%,附件类支持值为74%,其他设备类支持值为77%,胶水和粘合剂类支持值为72%。利用Apriori算法得到2条规则,分别是文具的先行值和后续附件,得到的支持值为65%,置信度为84.4%。其次是附件的先行项,书写工具的后项,支持值为65%,置信度为88.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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