使用k -意义方法对墨水购买数据进行分组

Susliansyah Susliansyah, Heny Sumarno, Hendro Priyono, N. Hikmah
{"title":"使用k -意义方法对墨水购买数据进行分组","authors":"Susliansyah Susliansyah, Heny Sumarno, Hendro Priyono, N. Hikmah","doi":"10.30645/J-SAKTI.V3I2.156","DOIUrl":null,"url":null,"abstract":"PT. Mayer Indah Indonesia is engaged in the production of goods, where the most important part to prepare the needs for production needs is the purchasing department, but in the purchasing section it is difficult to determine which items must be bought a lot, are and few in meeting the demand requirements of each part because of the needs goods for production are very unpredictable, eventually causing some goods demand not to be fulfilled because the goods are out of stock. To solve the problems experienced by the purchasing part, datamining using clustering algorithm is k-means method, where the initial stages determine the centroid randomly and do the first iteration calculation and determine the new centroid from the first iteration, then the second iteration calculation is done, because the results of the first and second iterations in the smallest layout of the three groups, the calculation stops. The results obtained by using the ink purchase data seen from the three attributes of incoming goods, items purchased and stock of goods, making it easier and help the purchasing department in classifying items that must be purchased a lot, medium and little.","PeriodicalId":402811,"journal":{"name":"J-SAKTI (Jurnal Sains Komputer dan Informatika)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Pengelompokkan Data Pembelian Tinta Dengan Menggunakan Metode K-Means\",\"authors\":\"Susliansyah Susliansyah, Heny Sumarno, Hendro Priyono, N. Hikmah\",\"doi\":\"10.30645/J-SAKTI.V3I2.156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PT. Mayer Indah Indonesia is engaged in the production of goods, where the most important part to prepare the needs for production needs is the purchasing department, but in the purchasing section it is difficult to determine which items must be bought a lot, are and few in meeting the demand requirements of each part because of the needs goods for production are very unpredictable, eventually causing some goods demand not to be fulfilled because the goods are out of stock. To solve the problems experienced by the purchasing part, datamining using clustering algorithm is k-means method, where the initial stages determine the centroid randomly and do the first iteration calculation and determine the new centroid from the first iteration, then the second iteration calculation is done, because the results of the first and second iterations in the smallest layout of the three groups, the calculation stops. The results obtained by using the ink purchase data seen from the three attributes of incoming goods, items purchased and stock of goods, making it easier and help the purchasing department in classifying items that must be purchased a lot, medium and little.\",\"PeriodicalId\":402811,\"journal\":{\"name\":\"J-SAKTI (Jurnal Sains Komputer dan Informatika)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J-SAKTI (Jurnal Sains Komputer dan Informatika)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30645/J-SAKTI.V3I2.156\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J-SAKTI (Jurnal Sains Komputer dan Informatika)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30645/J-SAKTI.V3I2.156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

印尼PT。梅耶Indah从事商品的生产,在最重要的部分准备生产需要的是采购部门的需求,但在采购部分很难确定哪些物品必须买了很多,和一些会议的需求要求每个部分因为需要商品的生产非常难以预测,最终导致有些商品需求不被满足,因为货物脱销。针对采购部分遇到的问题,采用聚类算法进行数据挖掘采用k-means方法,其中初始阶段随机确定质心并进行第一次迭代计算,从第一次迭代开始确定新的质心,然后进行第二次迭代计算,由于第一次和第二次迭代的结果在三组中布局最小,因此计算停止。从进货、已购物品、库存物品三个属性来看,使用油墨采购数据得出的结果,便于采购部门对必须购买的物品进行多、中、少的分类。
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
Pengelompokkan Data Pembelian Tinta Dengan Menggunakan Metode K-Means
PT. Mayer Indah Indonesia is engaged in the production of goods, where the most important part to prepare the needs for production needs is the purchasing department, but in the purchasing section it is difficult to determine which items must be bought a lot, are and few in meeting the demand requirements of each part because of the needs goods for production are very unpredictable, eventually causing some goods demand not to be fulfilled because the goods are out of stock. To solve the problems experienced by the purchasing part, datamining using clustering algorithm is k-means method, where the initial stages determine the centroid randomly and do the first iteration calculation and determine the new centroid from the first iteration, then the second iteration calculation is done, because the results of the first and second iterations in the smallest layout of the three groups, the calculation stops. The results obtained by using the ink purchase data seen from the three attributes of incoming goods, items purchased and stock of goods, making it easier and help the purchasing department in classifying items that must be purchased a lot, medium and little.
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
Rancang Bangun Pengatur Suhu Dan Kelembaban Ruang Server Berbasis IoT Pengelompokkan Data Pembelian Tinta Dengan Menggunakan Metode K-Means Pengembangan Model Sistem Pendukung Keputusan Dengan Kombinasi Metode Fuzzy Tahani Dan Topsis Dalam Penilaian Kinerja Instruktur Sistem Monitoring Lulusan Perguruan Tinggi Dalam Memasuki Dunia Kerja Menggunakan Tracer Study Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa
×
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