Clustering Profil Pengunjung Perpustakaan Menggunakan Algoritma K-Means

(Studi Kasus Perpustakaan BP Batam)

  • Fauziah Mahmuda Politeknik Negeri Batam
  • Maya Armys Roma Sitorus Institute of Technology Sepuluh Nopember
  • Hilda Widyastuti Politeknik Negeri Batam
  • Dwi Ely Kurniawan Politeknik Negeri Batam
Keywords: K-Means Algorithm, Clustering, Libraries

Abstract

Business Entity library Batam (BP Batam) is a public library located in Batam city with thw number of visitors. Every visitor who comes to do the charging guest book manually by writing system. It causes a buildup of data which are not organized. Data mining is one of the analytical tools that can be used to address the backlog of data. The method of Clustering with the K-Means Algorithm used in analyzing the data library visitors BP Batam. Library visitors using the data processing method of Elbow to get the best number of clusters K i.e., K = 3, and by using the center point (centroid) initial i,e, P1 = (4,1), P2 = (2,4), P3 = (4,2). The purpose of this research is to apply the algorithm for K-Means clustering in the data library visitors (case study library BP Batam). K-Means clustering results obtained from 1556 dataset data library visitors are grouped into three clusters, Clusters 1 is dominated by a college student and visitor located at Batam Center, Cluster 2 is dominated by a college student and visitor located at Bengkong, Cluster 3 is dominated by public and visitor status in Batam Center.

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References

Agusta, Y., 2007, K-means - Penerapan, Permasalahan dan Metode Terkait, Jurnal Sistem dan Informatika Vol. 3 (Februari 2007): 47-60.

Bertalya., 2009, Data Mining & Knowledge Discovery in Database, Universitas Gunadarma.

Bholowalia, Purnima., Kumar, Arvind., 2014, EBK-Means: A Clustering Techiniques based on Elbow Method and K-Means in WSN, International Journal of Computer Application (0975-8887), IX(105), pp. 17-24.

Cios, Krzysztof J., Lukasz A. Kurgan., 2002, Trends in Data Mining and Knowledge Discovery.

Han, Jiawei., Kamber, Micheline., 2006, Data Mining: Concepts and Techniques, Morgan Kaufman: San Fransisco.

Hilda, Widyastuti., 2012, Modul Pembelajaran Data Mining, Perpustakaan Politeknik Negeri Batam, Batam.

Kodinariya, T, M., Makwana, P, R., 2013, Review on determining number of cluster in K-Means Clustering, International Journal of Advance Research in Computer Science and Management Studies, I(6), pp. 90-95.

Lasa HS., 2007, Manajemen Perpustakaan Sekolah, Yogyakarta: Pinus Book Publisher.

Madhulatha, T. S., 2012, An Overview On Clustering Methods, IOSR Journal of Engineering, II(4), pp.719-725.

Michael Berry., Gordon S. Linoff., 2004, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, John Willey & Sons, Inc.

Muchlisin, R., 2012, Pengertian, Jenis dan Tujuan Perpustakaan.

Santoso, S., 2010, Statistik Multivariat, Jakarta: Elex Media Komputindo.

Supranto, J., 2004, Analisis Multivariat: Arti Dan Interpretasi, Jakarta: Rineka Cipta.

Sutarno NS., 2006, Perpustakaan dan Masyarakat, Jakarta: Sagung Seto.

Widya, S.A., Dedy, A., 2016, Pengelompokkan Minat Baca Mahasiswa menggunakan Metode K-Means, Teknik Informatika, Jurnal Ilmiah ILKOM Vol.8 No.2, Universitas Muslim Indonesia.

Yudho, GS., 2003, Penerapan Data Mining, Artikel Populer IlmuKomputer.com.

Published
2018-10-25
How to Cite
[1]
F. Mahmuda, M. Sitorus, H. Widyastuti, and D. Kurniawan, “Clustering Profil Pengunjung Perpustakaan Menggunakan Algoritma K-Means”, JAIC, vol. 1, no. 1, pp. 14-21, Oct. 2018.
Section
Articles