棕榈油收获数据聚类的数据挖掘算法实现

Widya Juli Mawaddah, I. Gunawan, Ika Purnama Sari
{"title":"棕榈油收获数据聚类的数据挖掘算法实现","authors":"Widya Juli Mawaddah, I. Gunawan, Ika Purnama Sari","doi":"10.55123/jomlai.v1i1.163","DOIUrl":null,"url":null,"abstract":"Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. In this study, we will discuss oil palm yields at PPKS Marihat, one of the Oil Palm Research Center branches located in Simalungun Regency, Medan, North Sumatra. Know how it grows. The Clustering algorithm is used in K-Means. Using this method, the data will be grouped into 3 (three) Clusters, where the application of the K-Means Clustering process uses the Rapid Miner tools. The data used is data on oil palm harvests at PPKS Marihat in 2020, consisting of 100 data items. The results obtained are crop yields with an excellent value of 66 items, harvest data with a good deal of 32 items, and harvest data with a reasonably good value of 2 items, based on net total and gross amount for each region. Based on this, it can be concluded that the K-Means Algorithm can be used to Cluster oil palm yields at PPKS Marihat","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation of Data Mining Algorithm for Clustering of Palm Oil Harvested Data\",\"authors\":\"Widya Juli Mawaddah, I. Gunawan, Ika Purnama Sari\",\"doi\":\"10.55123/jomlai.v1i1.163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. In this study, we will discuss oil palm yields at PPKS Marihat, one of the Oil Palm Research Center branches located in Simalungun Regency, Medan, North Sumatra. Know how it grows. The Clustering algorithm is used in K-Means. Using this method, the data will be grouped into 3 (three) Clusters, where the application of the K-Means Clustering process uses the Rapid Miner tools. The data used is data on oil palm harvests at PPKS Marihat in 2020, consisting of 100 data items. The results obtained are crop yields with an excellent value of 66 items, harvest data with a good deal of 32 items, and harvest data with a reasonably good value of 2 items, based on net total and gross amount for each region. Based on this, it can be concluded that the K-Means Algorithm can be used to Cluster oil palm yields at PPKS Marihat\",\"PeriodicalId\":14854,\"journal\":{\"name\":\"JOMLAI: Journal of Machine Learning and Artificial Intelligence\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOMLAI: Journal of Machine Learning and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55123/jomlai.v1i1.163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55123/jomlai.v1i1.163","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

棕榈油是在印尼经济发展中具有战略性作用的种植商品之一。在本研究中,我们将讨论位于北苏门答腊岛棉兰市Simalungun Regency的油棕研究中心分支机构之一PPKS Marihat的油棕产量。知道它是如何成长的。聚类算法用于K-Means。使用这种方法,数据将被分成3(3)个簇,其中K-Means聚类过程的应用使用快速挖掘工具。使用的数据是PPKS Marihat在2020年的油棕收成数据,由100个数据项组成。根据每个地区的净总量和总金额,得到的结果是作物产量值为66项,收成数据值为32项,收成数据值为2项,收成数据值为2项。基于此,可以得出K-Means算法可以对PPKS Marihat油棕产量进行聚类
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
Implementation of Data Mining Algorithm for Clustering of Palm Oil Harvested Data
Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. In this study, we will discuss oil palm yields at PPKS Marihat, one of the Oil Palm Research Center branches located in Simalungun Regency, Medan, North Sumatra. Know how it grows. The Clustering algorithm is used in K-Means. Using this method, the data will be grouped into 3 (three) Clusters, where the application of the K-Means Clustering process uses the Rapid Miner tools. The data used is data on oil palm harvests at PPKS Marihat in 2020, consisting of 100 data items. The results obtained are crop yields with an excellent value of 66 items, harvest data with a good deal of 32 items, and harvest data with a reasonably good value of 2 items, based on net total and gross amount for each region. Based on this, it can be concluded that the K-Means Algorithm can be used to Cluster oil palm yields at PPKS Marihat
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
The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District Implementation of Data Mining Algorithm for Clustering of Palm Oil Harvested Data C4.5 Algorithm Classification for Determining Smart Indonesia Program Recipients at MIS Al-Khoirot Analysis of K-Means Algorithm for Clustering of Covid-19 Social Assistance Recipients Website-Based Budget Adjustment Information System at PT. Taspen (Persero) Denpasar Branch Office
×
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