Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means

customer segmentation, pillar K-Means, RFM

Authors

  • Abd Hadi STMIK Asia Malang

DOI:

https://doi.org/10.32815/jitika.v13i2.413

Keywords:

segementasi pelanggan, pillar K-Means, RFM

Abstract

Perusahan penyedia layanan internet service provider (ISP) memiliki jumlah pelanggan yang sangat banyak dan beragam. Dengan semakin banyak dan beraamnya jumlah pelanggan peusahaan akan sulit untuk mengetahui tipe pelanggan yang dimiliki oleh perusahaan. Akibatnya perusahaan akan kesulitan menerapkan strategi pemasaran yang tepat kepada konsumen. Dalam paper ini digunakan metode pillar K-means untuk melakukan segmentasi pelanggan. Algoritma pilar k-means untuk melakukan segmentasi pelanggaran . Algoritma Pillar merupakan metode optimasi untuk menentukan centroid awal dalam algoritma K-Means. Dengan mengoptimasi centroid awal maka akan menghasilkan cluster yang lebih baik . setelah memperoleh hasil cluster yang optimal selanjutnya tipe pelanggaran dianalisis dengan menggunakan metode RFM (Recency, Frequency, Montetery). Hasil penelitian ini menunjukan bahwa pllar K-means mampu mengoptimasi hasil cluster :  k = 4 dengan a = 0.5 dan b = 0.8 serta nilai silheoette 8 = 0.47103. Dari hasil segmentasi 150 pelangganan diperoleh tipe pelangganan yang terdiri Most Valuable Costmers (33) Most Growable Costomers (41), Migrators (23) dan Below Zero (53).

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References

Neethu baby and Priyanka L.T customer classification and prediction based on data mining technique. Inernational journal of emerging technology and advanced engineering, 2(12):314-318, December 2012.
R Ling and Yen D. Customer relationship management: An analysis framework and implementation strtegis. Journal of computer information Systems, 41:82-97, 2001.
E.W.T. Ngai,Li Xiu, and D.C.K. chau. Application of data mining technques in customer relationship management: A Lirature review and classification. Science direct expert Systems with applications, 36:2592-2602, 2009
Derya Birant. Data Mining Using RFM Analysis, knowledge oriented applications in data mining, capter 6, pages 91-108. InTech DOI: 10.5772/13683.Avaible from:http.//www.intechopen.com/books/knowledge-oriented-applications-in-data-mining/data-mining-using-rfm-anaysis, 2011.
Kohei Arai and A. R. Barakbah. Hirarchical k-means; an algorithm for cendtroids initialization for kmeans. Reports of the faculty of science and engineering, saga university, 36(1); 25-31, 2007.
Shehroz S. Khan and Amir Ahmad. Cluster center initialization algorithm for k-means clustering. Science Direct, 15;1293-1302, 2004.
P.S Bradley and U.M Fayyad. Refining initial points for k-means clustering. Proc. 15th internat. Conf. on Machine Learing (ICMLS98), 1998
A.R Barakbah and Amir Ahmad. Cluster center initialization algorithem of intialcentroids optimization for k-means. In proc. Soft Compting, intelligent system, and information technology (SIIT) 2005, number 2-63-66. Petra Christian Uniersity , 2005
A.R Barakbah, A. Fariza, and Y. Setiowati. Optimization of initial centroids fr k-means using simuated annealing. In Proc. Idustra Electroics seminar (IES) 2005, pages 286-289. Electronic Engineering Polytechnic Institute of Surabaya-ITS, 2005.
A.R Barakba. A new algorithm fo optimization of k-means clustering with determining maximum distance between centroids. In proc. Industrial electronics seminar (IES) 2006, number 240-244. Electronic Engineering Polytechnic Istitute of Surabaya-ITS, 2006
A.R Barakbah and Y. Kiyoki. A pillar algorithm for k-means optimization by distance maximization for initial centroid designation. In Symposium on, pages 61-68, March 2009.
H. Rushmeir, R. Lawrence, and G. Almasi. Case study: visualizing customer segmentation based on cluster analysis. In computer Science and Information Processng (ICMSE), 2012 International Conference on, pages 1189-1182, 2012
Cai Qiuru, Lou Ye, Xi Haixu, Liu Yijun, And Zhu Guangping. Telecom costumer segmentation based on cluster analiysis. In Computer Science and Information Processing (CSIP), 2012 International Conference on, pages 103-109, 2019
Zhao han, zhang xiao –hang, wang Qi ,zhang ze-cong, and wang cen-yue. Customer segmentation on mobile online behaviour. In management science & engineering (ICMSE), 2014 international conference on, pages 103-109,2014.
S.S sulluoglu. Segmenting customers with data mining techniques.in digital information, networking, and wireless communications (DINWC),2015 Thid Internatinal Conferences on, pages 154-159,2015
d.zakrzewska and J. murlewski. Clustering algorithms for bank customer segmentation. In intelligent systems design and applications, 2005.ISDA ’05. Proceedings. 5th INTERNATIONAL CONFERENCE ON PAGES 197-202,2005
Minghua han. Customer segmentation model based on retail consumer behaviour analysis. In intelligent information technology application workshops, 2008. IITAW ’08. International symposium on, pages 914-917, 2008.
R.C Blattberg, kim B-D., and S.A. neslin. Database Marketing: analysing and managing customers. Springer, 2008.
B.kovesi, J,M.boucher,and S.saoudi.Stochastic k-means algorithm for vector quantization. Pattern recognition lett, 22:603-610,2011.
Peter J. rousseeuw. Silhouettes:agraphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53-65, 1987.

Published

2019-10-10