Implementation of K-Means for Analysis of Factors Causing Consumer Satisfaction at Madani Hotel Medan City

Debi Masri; Adek Apriyandi; Baginda Harahap

doi:10.25008/bcsee.v3i2.1162

Authors

Debi Masri Entrepreneurship Study Program, Faculty of Economics & Business, Battuta University
Adek Apriyandi Entrepreneurship Study Program, Faculty of Economics & Business, Battuta University
Baginda Harahap Informatics Study Program, Faculty of Technology, Battuta University

DOI:

https://doi.org/10.25008/bcsee.v3i2.1162

Keywords:

Hotel Madani Guest, Satisfaction Questionnaire, K-Means Cluster, RapidMiner

Abstract

Madani Hotel is one of the hotels located in Medan City which is a place of lodging for regional, national and international tourists, because this Madani Hotel is very strategically located close to the City Center, Grand Mosque, and Maimun Palace, some of which have become city icons. own field. This study uses data mining techniques using the k-means cluster method with the rapidminer application to visualize the pattern of causes of guest satisfaction with Hotel Madani both from service, facilities, comfort and price at Madani Hotel Medan City, the purpose of this research is to support and provide presentations for owners and Madani Hotel manager so that it can be used to assess how to increase the number of visits or guests at Madani Hotel Medan City. Based on research conducted, Madani Hotel's guest satisfaction score for comfort = 11, service = 14, facilities = 13, and price = 12.

Downloads

Download data is not yet available.

References

J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A k-means clustering algorithm,” J. R. Stat. Soc. Ser. c (applied Stat., vol. 28, no. 1, pp. 100–108, 1979.

A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,” Pattern Recognit., vol. 36, no. 2, pp. 451–461, 2003.

D. T. Pham, S. S. Dimov, and C. D. Nguyen, “Selection of K in K-means clustering,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 219, no. 1, pp. 103–119, 2005.

T. M. Kodinariya and P. R. Makwana, “Review on determining number of Cluster in K-Means Clustering,” Int. J., vol. 1, no. 6, pp. 90–95, 2013.

P. S. Bradley, K. P. Bennett, and A. Demiriz, “Constrained k-means clustering,” Microsoft Res. Redmond, vol. 20, no. 0, p. 0, 2000.

S. Na, L. Xumin, and G. Yong, “Research on k-means clustering algorithm: An improved k-means clustering algorithm,” in 2010 Third International Symposium on intelligent information technology and security informatics, 2010, pp. 63–67.

P. S. Bradley and U. M. Fayyad, “Refining initial points for k-means clustering.,” in ICML, 1998, vol. 98, pp. 91–99.

K. P. Sinaga and M.-S. Yang, “Unsupervised K-means clustering algorithm,” IEEE access, vol. 8, pp. 80716–80727, 2020.

D. Steinley, “K-means clustering: a half-century synthesis,” Br. J. Math. Stat. Psychol., vol. 59, no. 1, pp. 1–34, 2006.

K. Teknomo, “K-means clustering tutorial,” Medicine (Baltimore)., vol. 100, no. 4, p. 3, 2006.

K. Wagstaff, C. Cardie, S. Rogers, S. Schrödl, and others, “Constrained k-means clustering with background knowledge,” in Icml, 2001, vol. 1, pp. 577–584.

J. Burkardt, “K-means clustering,” Virginia Tech, Adv. Res. Comput. Interdiscip. Cent. Appl. Math., 2009.

T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881–892, 2002.

I. Bin Mohamad and D. Usman, “Standardization and its effects on K-means clustering algorithm,” Res. J. Appl. Sci. Eng. Technol., vol. 6, no. 17, pp. 3299–3303, 2013.

K. Alsabti, S. Ranka, and V. Singh, “An efficient k-means clustering algorithm,” 1997.

K. A. A. Nazeer and M. P. Sebastian, “Improving the Accuracy and Efficiency of the k-means Clustering Algorithm,” in Proceedings of the world congress on engineering, 2009, vol. 1, pp. 1–3.

L. Morissette and S. Chartier, “The k-means clustering technique: General considerations and implementation in Mathematica,” Tutor. Quant. Methods Psychol., vol. 9, no. 1, pp. 15–24, 2013.

J. Xu and K. Lange, “Power k-means clustering,” in International conference on machine learning, 2019, pp. 6921–6931.

Y.-M. Cheung, “k?-Means: A new generalized k-means clustering algorithm,” Pattern Recognit. Lett., vol. 24, no. 15, pp. 2883–2893, 2003.

H. Zha, X. He, C. Ding, M. Gu, and H. Simon, “Spectral relaxation for k-means clustering,” Adv. Neural Inf. Process. Syst., vol. 14, 2001.

B. Harahap, “Penerapan Algoritma K-Means Untuk Menentukan Bahan Bangunan Laris (Studi Kasus Pada UD. Toko Bangunan YD Indarung),” Ready Star, vol. 2, no. 1, pp. 394–403, 2019.

B. Harahap and A. Rambe, “Implementasi K-Means Clustering Terhadap Mahasiswa yang Menerima Beasiswa Yayasan Pendidikan Battuta di Universitas Battuta Tahun 2020/2021 Studi Kasus Prodi Informatika,” Informatika, vol. 9, no. 3, pp. 90–97, 2021, doi: 10.36987/informatika.v9i3.2185.

“K-Means Analysis in Grouping Abilities of Battuta University Informatics Study Program Students”