APPLICATION OF THE K-MEANS ALGORITHM FOR MANAGEMENT SUPPLIES IN THE LIBRARY

Authors

  • Andi Syahrul Ramdana Universitas Amikom Yogyakarta
  • Kusrini Universitas Amikom Yogyakarta
  • Eko Pramono Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.51401/jinteks.v6i1.3911

Keywords:

K-Means Algorithm, Inventory Management, Library, Data Mining

Abstract

This study explores the application of the K-Means algorithm to enhance inventory management efficiency in libraries. Faced with the challenge of meeting critical stock needs, the K-Means algorithm was selected for its advantages in ease of implementation and execution speed. Utilizing the K-Means method and RFM (Recency, Frequency, Monetary) approach, this research achieved a 70% accuracy rate in item clustering, while accuracy increased further without RFM. The analysis indicates the effectiveness of the K-Means algorithm in classifying and predicting stock needs, providing insights for better inventory management. This research contributes to understanding how data mining techniques can be leveraged to improve and enhance inventory management decisions in the library sector, with potential applications in other sectors..

 

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Published

2024-02-29

How to Cite

[1]
Andi Syahrul Ramdana, Kusrini, and E. Pramono, “APPLICATION OF THE K-MEANS ALGORITHM FOR MANAGEMENT SUPPLIES IN THE LIBRARY”, JINTEKS, vol. 6, no. 1, pp. 109-114, Feb. 2024.

Issue

Vol. 6 No. 1 (2024): EDISI 19

Section

Articles

Copyright (c) 2024 Andi Syahrul Ramdana, Kusrini, Eko Pramono

Creative Commons License

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