Implementation of Data Mining Clustering Using the K-Medoids Method in Grouping Library Books Politeknik Negeri Balikpapan

  • Maria Ulfah Electrical Engineering Department, Balikpapan State Polytechnic
  • Andi Sri Irtawaty Electrical Engineering Department, Balikpapan State Polytechnic

Abstract

Politeknik Negeri Balikpapan’s Library, annually adds to its collection of reading books. In order to procure the book according to the needs of users, namely students, information on the collection of books needed or in demand is needed. To answer this problem, it is necessary to have a clustering system for existing books in the library by looking at the aspect of borrowing frequency. The Clustering system is made using the K-Medoids method with the selection of 3 Clusters, namely, very attractive, desirable and less desirable. From the results of data processing through the Rapid Miner Application with K = 3 , the results obtained are cluster_0 (low) consisting of 97 book titles with the frequency of borrowing books in the rare category in other words less desirable to borrow, cluster_1 (high) consists of 2 book titles which are the most in-demand group of books namely Teknologi Bahan dan Teori dan Praktik Hotel Front Office. Cluster_2 (medium) consisting of 8 titles which are books with moderate borrowing frequenc.  The results of the performance obtained Davies Bouldin index value of 0.287. The results of grouping the data of these books can be used as input for library managers in procuring book collections based on the frequency of borrowing books.

Keywords: Clustering, Data mining, K-Medoids, Rapid Miner

Downloads

Download data is not yet available.

References

Intan fitri andyni, “Grouping of book borrowers using the k-means method at the Central Library of Upn veterans”, East Java, 2013.

D. Dwinavinta, “Klasterisasi judul buku dengan Metode K-Means”. Universitas Islam Indonesia, 2014.

Y. Hilda, “Perbandiangan K-Means dan k-Medoidss Clustering terhadap Kelayakan puskesmas di DIY Tahun 2015”. Universitas Islam Indonesia, 2016.

V. Aditya, “Perbandingan Algoritma K-Means dan K-Medoids dalam Pengelompokan komoditas peternakan Jateng”, 2015.

Kusrini and Lutfi, ET, “Data Mining Algorithms”, Yogyakarta: Andi Offset, 2009.

Pramudiono, What is Data Mining ?, [online], accessed on March 15, 2, 2006.

M. Ainur, et all, “Pengelompokan Kategori Buku Berdasarkan Judul Menggunakan Algoritma Agglomerative Hierarchical Clustering Dan K-Medoids”, JINACS Vol 02 No.03.

R. Kristini, “Implementasi Algoritma K-Medoids dalam Pengelompokan Mahasiswa yang Layak Mendapat Bantuan UKT”, Insologi Vol 1 No 2, 2022.

S. Atmiatun, et all “Penerapan Metode K-Medoids Untuk Pengelompokan Jalan di Kota Semarang”, Jurnal Teknik Informatika dan Sistem informasi, Vol 6 No 2 2020.

A. Supriyadi. et all, “Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas”, JIPI Vol 6 No 2 2021.

Published
2022-10-11
How to Cite
[1]
M. Ulfah and A. Irtawaty, “Implementation of Data Mining Clustering Using the K-Medoids Method in Grouping Library Books Politeknik Negeri Balikpapan”, JurnalEcotipe, vol. 9, no. 2, pp. 183-191, Oct. 2022.
Abstract viewed = 42 times
PDF downloaded = 40 times