Pengelompokan Hasil Panen Kelapa Sawit Dalam Produksi Per Blok Menggunakan Algoritma K-Means

Grouping of Palm Oil Harvests in Production Per Block Using the K-Means Algorithm

  • Agustina Lili * Mail STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Suhada STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Saputra Widodo STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Keywords: Hasil Panen kelapa sawit; K-Means; palm oil; Data grouping

Abstract

The palm oil yield is essential for a palm oil company, especially at PT. Surya Intisari Raya Sei Mandau. This is evidenced when the author tries to randomly determine the starting point of the cluster center from one of the computational starting objects. The number of cluster memberships generated is the same when using other data as the starting point for the cluster center. However, this only affects the amount of literacy carried out. Data grouping (object Clustering) is one of the processes of object mining that aims to partition existing data into one or more data clusters based on their characteristics. The K-Means algorithm in oil palm yields in production per block based on the variables formed per block for each PT. Surya Intisari Raya Sei Mandau. Grouping of oil palm yields at PT. Surya Intisari Raya Sei Mandau using the K-Means method is carried out through 4 stages, namely: name per block, year of plant, afdeling, and area (Ha) and is divided into 3 clusters, namely cluster 1 high, cluster 2 medium, and cluster 3 low.

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Published
2022-02-01
How to Cite
[1]
A. Lili, Suhada, and S. Widodo, “Pengelompokan Hasil Panen Kelapa Sawit Dalam Produksi Per Blok Menggunakan Algoritma K-Means”, J. Mach Learn. Data Anal., vol. 1, no. 1, pp. 45-54, Feb. 2022.
Issue
Vol 1 No 1 (2022): February
Section
Articles

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