Open AccessPenerapan Data Mining Dalam Pengelompokan Data Member Card Mitra10 Untuk Meningkatkan Rewards Terhadap Konsumen dengan Metode Fuzzy Subtractive Clustering
Author(s) -
Yudi Wibowo
Publication year - 2022
Publication title -
terapan informatika nusantara
Language(s) - English
Resource type - Journals
ISSN - 2722-7987
DOI - 10.47065/tin.v2i8.993
Subject(s) - cluster analysis , cluster (spacecraft) , point (geometry) , computer science , k medians clustering , data mining , fuzzy clustering , center (category theory) , subtractive color , mathematics , artificial intelligence , cure data clustering algorithm , computer network , physics , chemistry , geometry , optics , crystallography
Data clustering of Mitra10 member card, it is only based on the payment day by customer. And the total point only based on 0,25% calcutation of each payment. This is not so efficient knowing that there are so many ungrouping data that can be use for increase the rewards to the customer. In order to clustering the data of member card total point, it need a method of data clustering. The method election really affecting the result of data clustering. After all of the member card Mitra10 data transformed to the number form, then the datas can be grouping to fuzzy subtractive clustering algorythm. The data need to divided to some cluster : Select the amount of the cluster. In this research the data will be divided to 3 cluster. Select the center point of each cluster and fisrt center point selected randomly to generate main center point of each cluster. One data will be part of one cluster that has the smallest distance from the center cluster. Example for the first data, smallest distance get by cluster 1, so that the first data will be member of cluster 1. And so for the second data , smallest distance in cluster 3, so it will be member of cluster 3. In this iteration, center point of each cluster does not changed and there is no moving data to another cluster. Cluster 1 result: Has center (1,413, 1,195, 1,695) cluster 1 dominated by the bronze group. Cluster 2 result: Has center (2,658, 1,219, 1,585) cluster 2 dominated by the silver group. Cluster 3 result: Has center (4, 1,8, 3) cluster 3 dominated by the silver group