Eva Hermika, NPM 2009100022 (2024) APPLICATION OF DATA MINING IN SELECTING SUPERIOR PRODUCTS USING THE K-MEANS AND K-MEDOIDS ALGORITHM METHODS. Tugas_Akhir (Artikel) INFORMATIKA Universitas Labuhanbatu, 12 (3). pp. 364-372. ISSN 2615-1855 (e-ISSN)/ 2303-2863 (p-ISSN)
![[img]](http://repository.ulb.ac.id/style/images/fileicons/text.png) |
Text
COVER FULL.pdf Download (3MB) |
|
Text
LOA.pdf Download (154kB) |
|
Text
ARTIKEL.pdf Download (748kB) |
Abstract
As a supermarket, we are committed to always improving everything, including selecting the greatest goods. To evaluate which items are more superior or popular and which are less popular, you will want a sizable amount of information sources. To select products and identify those that belong in the superior product cluster, researchers employed the clustering method. The clustering strategy uses two forms of cluster analysis, k-means and k-medoids, which have related techniques. The research results show that the k-means algorithm's Davies Bouldin value is -0.430, whereas the kmedoids algorithm's Davies Bouldin value is -1.392. This suggests that the Davies Bouldin value of the k-medoids approach is the lowest, showing that the grouping findings of the k-means method are a better method to apply to the issue of choosing better products. Keywords : K-Means; K-Medoids; Clustering; Algorithm; Data Mining.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | K-Means; K-Medoids; Clustering; Algorithm; Data Mining |
| Subjects: | T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 21 Aug 2024 04:40 |
| Last Modified: | 21 Aug 2024 04:40 |
| URI: | http://repository.ulb.ac.id/id/eprint/932 |
Actions (login required)
 |
View Item |
