Penerapan Clustering K-Means Untuk Mendukung Pengelolaan Koleksi Pada Perpustakaan Fakultas Teknik Universitas Negeri Gorontalo

  • Indhitya Padiku Universitas Negeri Gorontalo
  • Agus Lahinta Universitas Negeri Gorontalo


One of the facility units that acts as a supporter of student learning in a university is the library. The library is a place where there are various collections of libraries that can enrich the knowledge of visitors. Thus, the management of additional collections is important in supporting this. Currently, the addition of book collections in the UNG Engineering Faculty Library is only based on references from statistical data on best-selling books, lecturer recommendations and the latest books without considering which collections are of interest to students. This can have a negative impact if there are additional collections in categories or types of books that are less desirable to read which can cause problems from the service side and financial losses of the library. Moving on from this problem, the K-Means clustering method will be applied to classify the reading interest of library visitors in this case are students. The supporting variables that will be used in the grouping will be divided according to the origin of the student majors by category of books, stock, number of books borrowed and number of books read. This grouping is expected to be a recommendation for library managers in managing the addition of book collections in the UNG Faculty of Engineering library.

Keywords: Clustering, Clustering K-Means, collection, library


Download data is not yet available.


Azis, W. S., dan Atmajaya, D, 2016, Pengelompokan Minat Baca Mahasiswa menggunakan Metode K-Means, Jurnal Ilmiah ILKOM, Vol. 8, No. 2, 89-94.

Can, F., E. A., 1990. Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases. ACM Transactions on Database Systems 15 (4), 483 - 517.

Kumar, V., Steinbach, M., Tan, P., 2004. Introduction to Data Mining. Addison-Wesley

Larose, D.T., 2005. Discovering Knowledge in Data : An Introduction to Data Mining. WileyInterscience, Canada.

Nugraha, D., D., C., Fahmi, M., Naimah, Z., dan Setiani, N., 2014, Seminar Nasional Aplikasi Teknologi Informasi, G1-G4.

Pulukadang, D. R., Mustafid, dan Farikhin, 2015, Pendekatan Clustering untuk Ekstraksi Pengetahuan pada Pembangunan Sistem Manajemen Pengetahuan, Jurnal Sistem Informasi Bisnis, Vol. 5, No. 2, 79-83.

Wibisono, Y., 2011. Perbandingan Partition Around Medoids (PAM) dan K-means Clustering untuk Tweets, Prosiding Konferensi Nasional Sistem Informasi. Medan, Februari 25-26, 483 – 486.

Xie, T., Liu, R., dan Wei, Z., 2020, Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data, Applied Mathematics and Nonlinear Sciences, Vol.5, Issue 1, 1-10.

How to Cite
Padiku, I., & Lahinta, A. (2022). Penerapan Clustering K-Means Untuk Mendukung Pengelolaan Koleksi Pada Perpustakaan Fakultas Teknik Universitas Negeri Gorontalo. Jurnal Teknik, 20(1), 54-62.
Abstract Views : 142 | DOWNLOAD PDF Views : 470