Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

  • Solikhun Solikhun AMIK Tunas Bangsa, Pematangsiantar, Sumatera Utara
  • Verdi Yasin STMIK Jayakarta, Jakarta
  • Donni Nasution Universitas Prima Indonesia, Medan
Abstract views: 70 , PDF downloads: 43
Keywords: Data mining, Clustering, K Means, Egg Production, DBI Value

Abstract

The need for eggs that continues to increase will not increase with large egg production so that there is a shortage of egg supplies which results in high egg prices. It is necessary to group egg production in Indonesia to find out which areas fall into the high cluster and which areas fall into the low cluster. This study aims to classify the egg production of laying hens in Indonesia. The method used is the K-Means Clustering method which is a popular clustering method. To find out how optimal the number of clusters in the K-Means method is for grouping egg production in Indonesia, the researcher evaluates the DBI value of each number of existing clusters. In this study, 8 clusters were used, namely 2 clusters, 3 clusters, 4 clusters, 5 clusters, 6 clusters, 7 clusters, 8 clusters, and 9 clusters. The results of measuring the DBI value are the number of clusters 2 = 0.215, the number of clusters 3 = 0.149, the number of clusters 4 = 0.146, the number of clusters 5 = 0.157, the number of clusters 6 = 0.180, the number of clusters 7 = 0.205, the number of clusters 8 = 0.192 and the number of clusters 9 = 0.154. This study shows that the best number of clusters is the number of clusters 4 with the smallest DBI value of 0.146.

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Published
2022-06-02
How to Cite
Solikhun, S., Yasin, V., & Donni Nasution. (2022). Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia. International Journal of Artificial Intelligence & Robotics (IJAIR), 4(1), 39-47. https://doi.org/10.25139/ijair.v4i1.4328
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Articles