利用 Apriori 算法分析货物库存,帮助做出货物采购决策

Nur Azis, Purwo Agus Sucipto, Agus Herwanto, Era Sari Munthe, Dola Irwanto
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引用次数: 0

摘要

covid-19大流行爆发后,covid-19大流行期间的不确定性指数居高不下。商业界除了受到巨大冲击外,买家的低迷兴趣也限制了其行动。在这种情况下,研究人员打算提供一个概述,以帮助商业人士,尤其是在购买有助于填补仓库库存的商品时。为了获得最大的结果和最小的错误率。研究人员使用 Apriori 算法分析库存物品,并使用 Tanagra 1.4 版应用程序。研究数据使用的是过去 1 年的销售记录,这里使用的数据是 2022 年 5 月至 2023 年 4 月之间的数据。项目集总数为 375 个。但在应用黄金法则(阈值)后,只有 10 种产品的销售额达到了 1623 件。这项研究根据已确定的最小支持度和最小置信度得出了最终的有序关联,即 12 条规则与置信度为 100% 的 2 个项目集的组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Goods Stock Using the Apriori Algorithm to Aid Goods Purchase Decision Making
After the covid-19 pandemic outbreak and the high uncertainty index during the covid-19 pandemic. The business world is experiencing a huge impact in addition to the sluggish interest of buyers is also limited in its movement. On this occasion, the researcher intends to provide an overview that can help business people, especially in purchasing goods that are useful for filling the stock of goods in the warehouse. To get maximum results and minimum error rate. Researchers use the Apriori Algorithm in analyzing stock items and use the Tanagra version 1.4 application. Research data used the sales history of the past 1 year here the data used is between May 2022 and April 2023. With a total itemset of 375. But after applying the Golden Rule (threshold), there are only 10 products with sales reaching 1623 items. This research produces a final ordered association based on the minimum support and minimum confidence that has been determined, namely 12 rules with a combination of 2 itemsets with a confidence value of 100%.
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