Analysis of Student Reading Interest in UNSIKA Library with K-Means Algorithm

  • Syela Herdina Naibaho Universitas Singaperbangsa Karawang
  • Nailufar Farha Afifah Universitas Singaperbangsa Karawang
  • Yuyun Umaidah Universitas Singaperbangsa Karawang
  • Nono Heryana Universitas Singaperbangsa Karawang
Abstract views: 69 , PDF downloads: 54
Keywords: Clusterization, K-Means, Reading Interest, Library, RapidMiner

Abstract

This study aims to analyze students' reading interest at the Singaperbangsa Karawang University Library using the K-Means algorithm. The K-Means method is used to group books based on borrowing patterns, so as to provide better insight into students' reading preferences and assist libraries in managing book collections. Book borrowing data was obtained from the university librarian database, while literature study was carried out by studying and looking for references in related journals and literature. After the data is clean and can be processed, the clustering process is carried out using the K-Means algorithm by determining the value of k. The clustering results are displayed in the form of a table showing the groups of books that are frequently borrowed, books with a moderate borrowing frequency, and books that are rarely borrowed by students. Cluster quality evaluation was carried out using the Davies Bouldin value. The findings of this study indicate that the K-Means algorithm is effective in classifying books based on borrowing patterns. With a better understanding of students' reading interest, libraries can optimize the placement of books, increase the availability of books of interest, and develop strategies to increase overall student reading interest. The results of this study make a significant contribution to the development of university libraries and provide useful guidelines for decision-making in library management.

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References

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Published
2024-05-18
How to Cite
[1]
S. H. Naibaho, Nailufar Farha Afifah, Yuyun Umaidah, and Nono Heryana, “Analysis of Student Reading Interest in UNSIKA Library with K-Means Algorithm”, antivirus, vol. 18, no. 1, pp. 82-94, May 2024.
Section
Articles