PENERAPAN DATA MINING ALGORITMA K-MEANS CLUSTERING PADA POPULASI AYAM PETELUR DI INDONESIA

  • Elsa Ramadanti Universitas Nusa Putra Sukabumi
  • Muhamad Muslih Universitas Nusa Putra

Abstract

Chicken eggs are a type of egg that is easy to find and favored by many people. So that the community's need for chicken eggs is needed to meet the needs of animal protein sources and their daily nutrition. This study analyzes the application of the K-Means Clustering Algorithm Data Mining on the Layering Poultry Population in Indonesia. The source of data on the population of laying hens in Indonesia is obtained and collected through the website of the National Statistics Agency. The data used is from 2016-2020 which consists of 34 provinces. The data will be grouped into 3 clusters, namely high, medium, and low population clusters. Data processing is done manually in Microsoft Excel and assisted by data mining tools, namely Rapidminer and Orange. The results of the data processing show the same results, namely 1 province for the high population cluster, 3 provinces for the medium population cluster and 30 provinces for the low population cluster. The purpose of this study is for the government and breeders to pay more attention to the number of laying hens in Indonesia based on clusters that have been carried out to maintain a balance in the number and stability of egg prices in the community.

 

Keywords: Data Mining, K-Means, Clustering, Population, Laying Chicken

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Published
2022-01-10
How to Cite
[1]
E. Ramadanti and M. Muslih, “PENERAPAN DATA MINING ALGORITMA K-MEANS CLUSTERING PADA POPULASI AYAM PETELUR DI INDONESIA”, rabit, vol. 7, no. 1, pp. 1-7, Jan. 2022.
Section
Articles
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