Penerapan Data Mining Dalam Pengelompokan Data Member Card Mitra10 Untuk Meningkatkan Rewards Terhadap Konsumen dengan Metode Fuzzy Subtractive Clustering


  • Yudi Wibowo * Mail Universitas Budi Darma, Medan, Indonesia
  • (*) Corresponding Author
Keywords: Member Card; Data Mining; Fuzzy Subtractive Clustering

Abstract

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

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References

Vianti Mala Anggraeni Kusuma, M. Tanzil Furqon dan Lailil Muflikhah, “Implementasi Metode Fuzzy Subtractive Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan.”, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, No. e-ISSN: 2548-964X

I Gede Oka Artawan, G.K. Gandhiadi dan Tjokorda Bagus Oka, “Penentuan Lokasi SMP Baru di Kabupaten Klungkung dengan Algoritma Fuzzy Subtractive Clustering.”, Jurnal Matematika, Vol. 3 No. 2, Desember 2013. No. ISSN : 1693-1394

Kusrini dan Emha Taufiq Luthfi, ”Algoritma Data Mining” 2010 : 3

Goldie Gunadi, Dana Indra Sensuse, ”Discovering Knowledge in Data : An Introduction to Data Mining”

Feri Sulianta dan Dominikus Juju, “Data Mining - Meramalkan Bisnis Perusahaan”, 2010

Eko Prasetyo, ”Data Mining : Konsep dan Aplikasi Menggunakan Matlab”, 2010

Kusumadewi dan Purnomo (2004, 39)

Kusumadewi dan Purnomo dalam Yanti Novita (2006, 42)

Denis Aprilia C, Donny Aji Baskoro, Lia Ambarwati, I Wayan Simri Wicaksana, “Belajar Data Mining Dengan RapidMiner”, 2013


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Published: 2022-01-31
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