Implementation of K-Means Algorithm for Clustering Books Borrowing in School Libraries

Authors

  • Daud Siburian STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Sundari Retno Andani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Ika Purnama Sari STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i2.725

Keywords:

Implementation, Data Mining, K-Means, Book, Library

Abstract

The school library is an important resource in an effort to support the process of improving the quality of education in schools. Through the library a lot of information can be extracted and used for educational purposes. The library is expected to play its function as a vehicle for education, research, preservation, information, and recreation to improve the nation's intelligence. This study aims to cluster the borrowing of library books at SMA Assisi Pematangsiantar. The research data was obtained from the school library. The algorithm used for the clustering process is K-Means Clustering which is one of the data mining algorithms. The data was processed using Microsoft Excel and Rapid Miner 5.3 to determine the value of the centroid in 2 clusters, namely the highest and lowest clusters. Based on manual calculations with Microsoft Excel and testing with Rapid Miner, this study resulted in the same value, namely the highest cluster produced 6 types of books including Mathematics,. Geography, Chemistry, Civics, Physical Education and Computers. As for the lowest cluster, there are 6 types of books, namely Indonesian, English, Biology, Physics, Religion and Cultural Arts. So it can be concluded that the K-Means method in this study can cluster school library book borrowing well, referring to manual calculations and testing which have the same results

References

V. V. Sianipar, A. Wanto, and M. Safii, “Decision Support System for Determination of Village Fund Allocation Using AHP Method,” The IJICS (International Journal of Informatics and Computer Science) ISSN, vol. 4, no. 1, pp. 20–28, 2020.

R. Simarmata, R. W. Sembiring, R. Dewi, A. Wanto, and E. Desiana, “Penentuan Masyarakat Penerima Bantuan Perbaikan Rumah di Kecamatan Siantar Barat Menggunakan Metode ELECTRE,” Journal of Computer System and Informatics (JoSYC), vol. 1, no. 2, pp. 68–75, 2020.

R. Watrianthos, W. A. Ritonga, A. Rengganis, A. Wanto, and M. Isa Indrawan, “Implementation of PROMETHEE-GAIA Method for Lecturer Performance Evaluation,” Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012067, 2021.

S. R. Ningsih, D. Hartama, A. Wanto, I. Parlina, and Solikhun, “Penerapan Sistem Pendukung Keputusan Pada Pemilihan Objek Wisata di Simalungun,” in Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), 2019, pp. 731–735.

N. Nasution, G. W. Bhawika, A. Wanto, N. L. W. S. R. Ginantra, and T. Afriliansyah, “Smart City Recommendations Using the TOPSIS Method,” IOP Conference Series: Materials Science and Engineering, vol. 846, no. 1, pp. 1–6, 2020.

R. A. Hutasoit, S. Solikhun, and A. Wanto, “Analisa Pemilihan Barista dengan Menggunakan Metode TOPSIS (Studi Kasus: Mo Coffee),” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 2, no. 1, pp. 256–262, 2018.

I. M. Muhamad, S. A. Wardana, A. Wanto, and A. P. Windarto, “Algoritma Machine Learning untuk penentuan Model Prediksi Produksi Telur Ayam Petelur di Sumatera,” vol. 1, no. 4, pp. 126–134, 2022.

M. Mahendra, R. C. Telaumbanua, A. Wanto, and A. P. Windarto, “Akurasi Prediksi Ekspor Tanaman Obat , Aromatik dan Rempah-Rempah Menggunakan Machine Learning,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 6, pp. 207–215, 2022.

R. Puspadini, A. Wanto, and N. Arminarahmah, “Penerapan ML dengan Teknik Bayesian Regulation untuk Peramalan,” Journal of Computer System and Informatics (JoSYC), vol. 3, no. 3, pp. 147–155, 2022.

N. L. W. S. R. Ginantra, A. D. GS, S. Andini, and A. Wanto, “Pemanfaatan Algoritma Fletcher-Reeves untuk Penentuan Model Prediksi Harga Nilai Ekspor Menurut Golongan SITC,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, pp. 679–685, 2022.

N. Arminarahmah, S. D. Rizki, O. A. Putra, U. Islam, K. Muhammad, and A. Al, “Performance Analysis and Model Determination for Forecasting Aluminum Imports Using the Powell-Beale Algorithm,” IJISTECH (International Journal of Information System & Technology), vol. 5, no. 5, pp. 624–632, 2022.

N. L. W. S. R. Ginantra et al., “Performance One-step secant Training Method for Forecasting Cases,” Journal of Physics: Conference Series, vol. 1933, no. 1, pp. 1–8, 2021.

A. Perdana, S. Defit, and A. Wanto, “Optimalisasi Parameter dengan Cross Validation dan Neural Back-propagation Pada Model Prediksi Pertumbuhan Industri Mikro dan Kecil,” Jurnal Sistem Informasi Bisnis, vol. 01, no. 11, pp. 34–42, 2021.

N. L. W. S. R. Ginantra, M. A. Hanafiah, A. Wanto, R. Winanjaya, and H. Okprana, “Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012022, 2021.

A. Wanto, S. Defit, and A. P. Windarto, “Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana,” RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 254–264, 2021.

V. V. Utari, A. Wanto, I. Gunawan, and Z. M. Nasution, “Prediksi Hasil Produksi Kelapa Sawit PTPN IV Bahjambi Menggunakan Algoritma Backpropagation,” Journal of Computer System and Informatics (JoSYC, vol. 2, no. 3, pp. 271–279, 2021.

N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, “Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 012018, pp. 1–7, 2021.

J. Hutagalung, N. L. W. S. R. Ginantra, G. W. Bhawika, W. G. S. Parwita, A. Wanto, and P. D. Panjaitan, “COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm,” Journal of Physics: Conference Series, vol. 1783, no. 1, p. 012027, 2021.

N. A. Febriyati, A. D. GS, and A. Wanto, “GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020.

M. A. Hanafiah, A. Wanto, and P. B. Indonesia, “Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 315–322, 2020.

T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, and Z. M. Nasution, “Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 9–20, 2021.

A. Wanto et al., Data Mining : Algoritma dan Implementasi. Yayasan Kita Menulis, 2020.

W. T. C. Gultom, A. Wanto, I. Gunawan, M. R. Lubis, and I. O. Kirana, “Application ofThe Levenberg Marquardt Method In Predict The Amount of Criminality in Pematangsiantar City,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 21–29, 2021.

Z. S. Younus et al., “Content-based image retrieval using PSO and k-means clustering algorithm,” Arabian Journal of Geosciences, vol. 8, no. 8, pp. 6211–6224, 2015.

E. Prasetyo, Data Mining: Konsep dan Aplikasi menggunakan Matlab. Yogyakarta: Andi Offset, 2012.

D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining: Second Edition. New Jersey: John Wiley & Sons, 2005.

R. Primartha, Belajar Machine Learning Teori dan Praktik. Bandung: Informatika Bandung, 2018.

T. Khotimah, “Pengelompokan Surat dalam Al Qur’an menggunakan Algortima K-Means,” Jurnal Simetris, vol. 5, no. 1, pp. 83–88, 2014.

I. Parlina, A. P. Windarto, A. Wanto, and M. R. Lubis, “Memanfaatkan Algoritma K-Means dalam Menentukan Pegawai yang Layak Mengikuti Asessment Center untuk Clustering Program SDP,” CESS (Journal of Computer Engineering System and Science), vol. 3, no. 1, pp. 87–93, 2018.

Downloads

Published

2022-07-29

How to Cite

Siburian, D., Andani, S. R., & Sari, I. P. (2022). Implementation of K-Means Algorithm for Clustering Books Borrowing in School Libraries. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(2), 115–124. https://doi.org/10.55123/jomlai.v1i2.725

Issue

Vol. 1 No. 2 (2022): June

Section

Articles

License

Copyright (c) 2022 Daud Siburian, Sundari Retno Andani, Ika Purnama Sari

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:their article is original, written by the mentioned author(s),

  • has never been published before,
  • does not contain statements that violate the law, and
  • does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

  • Copyright and other proprietary rights related to the article, such as patents,
  • The right to use the substance of the article in its own future works, including lectures and books,
  • The right to reproduce the article for its own purposes,
  • The right to archive all versions of the article in any repository, and
  • The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published