Penerapan Algoritma Fuzzy C-Means Pada Penentuan Lokasi Gudang Pendukung PT. XYZ

Authors

  • Brian Christian Universitas Bunda Mulia
  • Lukman Hakim Universitas Bunda Mulia

DOI:

https://doi.org/10.24246/aiti.v16i1.31-48

Keywords:

Data Mining, Clustering, Fuzzy C-Means, Supporting Warehouse PT. XYZ

Abstract

In data mining a series of processes are applied to extract information from a data set. PT. XYZ which has a lot of sales data that can be processed. In the company PT. XYZ which is engaged in the retail sector which has tight competition and develops rapidly, customer satisfaction is one of the things that need to be considered. It takes more than product quality, but service to customers is also important to win competition in sales and one that can be considered in customer satisfaction is the availability of products that customers want. Use of Clustering Method to classify objects based on similarity in characteristics, especially one of fuzzy clustering, that is, fuzzy C-means can be used to determine the distance and presence of each point in a cluster. In this study, fuzzy C-means is applied in determining the supporting warehouse of PT. XYZ is based on the clustering location of PT. XYZ which is represented as the Cartesian coordinates and the centroid of each cluster refers to the location of the supporting warehouse along with the grouping of outlets with the supporting warehouse.Using 3 clusters in 100 iterations the difference in objective function is 3.3e-8%, while the experiment using 4 clusters requires 39 iterations with an objective function difference of 1.1e-12%,then themore number of clusters will minimize the difference in objective functions for results with smaller errors.

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References

Merliana, Ernawati and Santoso, “Analisa Penentuan Jumlah Cluster Terbaik Pada Metode K-means Clustering,” in Prosiding Seminar Nasional Multi disiplin ilmu & call for papers unisbank, 2016.

Lindawati, “Data Mining Dengan Teknik Clustering Dalam Pengklasifikasian Data Mahasiswa Studi Kasus Prediksi Lama Studi Mahasiswa Universitas Bina Nusantara,” Seminar Nasional Informatika, vol. 5, no. 1, 2008.

T. Meri, L. Linawati and A. Setiawan, “Penerapan Algoritma Fuzzy C-Means (FCM) Pada Penentuan Lokasi Pendirian Loket Pembayaran Air PDAM Salatiga,” Prosiding Seminar Nasional Sains dan Pendidikan Sains, vol. 4, no. 1, pp. 497 - 505, 2013.

H. Sulastri and A. Irham, “PENERAPAN DATA MINING DALAM PENGELOMPOKAN PENDERITA,” Jurnal Teknologi dan Sistem Informasi, vol. 3, no. 2, pp. 209-305, 2017.

J. C. Bezdek, R. Ehrlich and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Comput, Geosci, vol. 10, no. 2, pp. 191-203, 1984.

E. Prasetyo, Data Mining, Mengolah Data Menjadi Informasi Menggunakan Matlab, Yogyakarta: Penerbit Andi, 2014.

A. Kridanto and J. L. Buliali, “Metode Hibrida FCM dan PSO-SVR untuk Prediksi Data Arus Lalu Lintas,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 1, no. 3, pp. 302-311, 2015.

S. Kusumadewi and H. Purnomo, Aplikasi Fuzzy Untuk Pendukung Keputusan, 2nd ed., Yogyakarta: Graha Ilmu, 2010.

S. Kusumadewi and S. Hartati, Integrasi Sistem Fuzzy dan Jaringan Syaraf, 2nd ed., Yogyakarta: Graha Ilmu, 2010.

P. Hartono and N. Fauzi, “Pengendali Otomasi 3-Axis Berbasis PC pada Simulasi Proses Las,” Metal Indonesia, vol. 36, no. 1, 2014.

S. Sastra, Permodelan 2D dan 3D dengan Autocad, Jakarta: PT. Elex Media Komputindo, 2009, pp. 195-197.

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Published

2019-12-04

How to Cite

[1]
B. Christian and L. Hakim, “Penerapan Algoritma Fuzzy C-Means Pada Penentuan Lokasi Gudang Pendukung PT. XYZ”, AITI, vol. 16, no. 1, pp. 31–48, Dec. 2019.

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