| dc.contributor.author | Puspitasari, Novianti | |
| dc.contributor.author | Widians, Joan Angelina | |
| dc.contributor.author | Setiawan, Noval Bayu | |
| dc.date.accessioned | 2021-10-20T13:07:32Z | |
| dc.date.available | 2021-10-20T13:07:32Z | |
| dc.date.issued | 2020-04-30 | |
| dc.identifier.citation | IEEE | en_US |
| dc.identifier.issn | 2338-0403 | |
| dc.identifier.uri | http://repository.unmul.ac.id/handle/123456789/7432 | |
| dc.description | Peer Review: Jurnal Nasional Terakreditasi SINTA2. Joan Angelina Widians | en_US |
| dc.description.abstract | Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Department of Computer Engineering Universitas Diponegoro | en_US |
| dc.relation.ispartofseries | Volume 8, Issue 2, Year 2020 (April 2020);https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13295 | |
| dc.subject | bisecting k-means; customer segmentation; RFM; best cluster; silhouette coefficient | en_US |
| dc.title | Peer Review: Jurnal Nasional Terakreditasi_Widians_Segmentasi pelanggan menggunakan algoritme bisecting k-means berdasarkan model recency, frequency, dan monetary (RFM) | en_US |
| dc.type | Article | en_US |
