基于先验算法的工程材料采购预测方法的实现。Padat Karya Konstruksi

Nadia Elisa Suhardi, Maryaningsih Maryaningsih, Rizka Tri Alinse
{"title":"基于先验算法的工程材料采购预测方法的实现。Padat Karya Konstruksi","authors":"Nadia Elisa Suhardi, Maryaningsih Maryaningsih, Rizka Tri Alinse","doi":"10.53697/jkomitek.v2i1.792","DOIUrl":null,"url":null,"abstract":"In predicting the purchase of goods, there are many methods that can be used, among others, by processing purchase data using Data Mining method accompanied by Priori Algorithm based on the purchasing process carried out by the company based on the relationship between the goods purchased. By using the a priori algorithm, the company in this case is CV. Padat Karya Konstruksi can estimate the number of building materials needed by workers this is due to the large number of goods purchases at CV. Padat Karya Konstruksi to match the work. The stages of the a priori algorithm used are data transformation in tabular table form, determining the minimum value of support and minimum confidence, formation of 1-item set candidate combination pattern then counting the number of occurrences in each item set. So that it is obtained from 17 (seventeen) data, items that are often purchased are cement as much as 11 (eleven) times, split stone 8 (eight) times, and concrete sand as much as 8 (eight) times with a support value of 25% and a confidence value of 75%.","PeriodicalId":371693,"journal":{"name":"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Implementation Of Apriori Algorithm Methods For Predicting Project Material Purchases At CV. Padat Karya Konstruksi\",\"authors\":\"Nadia Elisa Suhardi, Maryaningsih Maryaningsih, Rizka Tri Alinse\",\"doi\":\"10.53697/jkomitek.v2i1.792\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In predicting the purchase of goods, there are many methods that can be used, among others, by processing purchase data using Data Mining method accompanied by Priori Algorithm based on the purchasing process carried out by the company based on the relationship between the goods purchased. By using the a priori algorithm, the company in this case is CV. Padat Karya Konstruksi can estimate the number of building materials needed by workers this is due to the large number of goods purchases at CV. Padat Karya Konstruksi to match the work. The stages of the a priori algorithm used are data transformation in tabular table form, determining the minimum value of support and minimum confidence, formation of 1-item set candidate combination pattern then counting the number of occurrences in each item set. So that it is obtained from 17 (seventeen) data, items that are often purchased are cement as much as 11 (eleven) times, split stone 8 (eight) times, and concrete sand as much as 8 (eight) times with a support value of 25% and a confidence value of 75%.\",\"PeriodicalId\":371693,\"journal\":{\"name\":\"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53697/jkomitek.v2i1.792\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53697/jkomitek.v2i1.792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在预测商品的购买情况时,可以使用的方法有很多,其中,根据公司根据所购商品之间的关系进行的购买过程,使用数据挖掘方法和Priori算法对购买数据进行处理。通过使用先验算法,本例中的公司是CV。Padat Karya Konstruksi可以估算出工人所需的建筑材料数量,这是由于CV购买了大量货物。Padat Karya Konstruksi的作品。先验算法采用表格形式进行数据转换,确定最小支持度和最小置信度,形成1项集候选组合模式,然后计算每个项集中出现的次数。因此从17(17)个数据中得出,经常购买的项目是水泥多达11(11)次,劈裂石8(8)次,混凝土砂多达8(8)次,支撑值为25%,置信度为75%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Implementation Of Apriori Algorithm Methods For Predicting Project Material Purchases At CV. Padat Karya Konstruksi
In predicting the purchase of goods, there are many methods that can be used, among others, by processing purchase data using Data Mining method accompanied by Priori Algorithm based on the purchasing process carried out by the company based on the relationship between the goods purchased. By using the a priori algorithm, the company in this case is CV. Padat Karya Konstruksi can estimate the number of building materials needed by workers this is due to the large number of goods purchases at CV. Padat Karya Konstruksi to match the work. The stages of the a priori algorithm used are data transformation in tabular table form, determining the minimum value of support and minimum confidence, formation of 1-item set candidate combination pattern then counting the number of occurrences in each item set. So that it is obtained from 17 (seventeen) data, items that are often purchased are cement as much as 11 (eleven) times, split stone 8 (eight) times, and concrete sand as much as 8 (eight) times with a support value of 25% and a confidence value of 75%.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Information System Of Student Point Violations (Case Study Of Sma Muhammadiyah 7 Serbelawan) Hotspot Network Security System From Brute Force Attack Using Pfsense External Firewall (Case Study of Wifi-Ku.Net Hotspot) Application Of 3-Dimensional Modeling In Android-Based Adventure Game Applications Application Of Data Mining Using The Naïve Bayes Classification Method To Predict Public Interest Participation In The 2024 Elections Graduation Book Information System Of Muhammadyah University Of Bengkulu
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1