Implementation of the K-Means Clustering Algorithm in Determining Productive Oil Palm Blocks at Pt Arta Prigel

Abstract

The purpose of this study is to implement the K-Means Clustering method to determine the patterns of productive oil palm production based on their blocks at Pt Arta Prigel. The research is motivated by issues within the oil palm blocks, such as the absence of productive block summaries, insufficient plantation land analysis, and erroneous decision-making. The development method utilizes CRISP-DM, with data spanning 2 years from October 2021 to October 2023. From the 1275 production records, after cleaning, 1015 records remain. Filtering the initial 51 blocks results in 37 blocks for the years 2021 and 2022, and 46 blocks for the year 2023. After clustering, the production outcomes for the year 2021 are as follows: cluster_0 has 34 blocks, cluster_1 has 10 blocks. For the year 2022, cluster_0 has 24 blocks, cluster_1 has 37 blocks. In the year 2023, cluster_0 has 44 blocks, cluster_1 has 27 blocks. The testing method employs the silhouette coefficient, and the silhouette score testing results indicate the formation of 2 clusters (K=2) with a value of 0.62, the results obtained from testing with 2 clusters indicate that the formed clusters are accurate. The findings of this study include patterns, graphs, and production tables generated using the K-Means Clustering method at Pt Arta Prigel.

Keywords

• k-means clustering
• rapid miner
• sawit
• silhouette coefficient