{"title":"Pemilihan Pelanggan Potensial Dengan Melakukan Pemetaan Area Dengan Metode Algoritma K-NN dan K-Means Di Yamaha Nusantara Motor Purwokerto","authors":"Diwahana Mutiara Candrasari","doi":"10.31331/joined.v4i2.1942","DOIUrl":null,"url":null,"abstract":"This study proposes a clustering of potential customers at Yamaha Nusantra Motor Purwokerto based on consumer characteristics. Clustering is one of the processes of data mining that aims to partition existing objects into one or more clusters of objects based on their characteristics. The method used in this study is K-Nearest Neighbor as a determination of the feasibility of the data while K-Means is used as customer clustering. The value of the Davies Bouldin Index was examined using rapidminer while the purity validation was using Microsoft excel. The results show that the K-Means training data of the Davies Bouldin Index is 0.266 and the Purity validation is 1.3351. In the K-Means data testing the results show the Davies Bouldin Index value of 0.298 and the Purity validation of 0.6631.","PeriodicalId":437760,"journal":{"name":"Joined Journal (Journal of Informatics Education)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Joined Journal (Journal of Informatics Education)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31331/joined.v4i2.1942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This study proposes a clustering of potential customers at Yamaha Nusantra Motor Purwokerto based on consumer characteristics. Clustering is one of the processes of data mining that aims to partition existing objects into one or more clusters of objects based on their characteristics. The method used in this study is K-Nearest Neighbor as a determination of the feasibility of the data while K-Means is used as customer clustering. The value of the Davies Bouldin Index was examined using rapidminer while the purity validation was using Microsoft excel. The results show that the K-Means training data of the Davies Bouldin Index is 0.266 and the Purity validation is 1.3351. In the K-Means data testing the results show the Davies Bouldin Index value of 0.298 and the Purity validation of 0.6631.