Implementasi Algoritma K-Means Clustering Dalam Menentukan Blok Tanaman Sawit Produktif Pada PT Arta Prigel - Anggriani, Yesi Pitaloka; Arif, Alfis; Febriansyah, Febriansyah (Jurnal Komtika (Komputasi dan Informatika), 2024)
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 2 blocks, and cluster_2 has 10 blocks. For the year 2022, cluster_0 has 36 blocks, cluster_1 has 8 blocks, and cluster_2 has 28 blocks. In the year 2023, cluster_0 has 39 blocks, cluster_1 has 8 blocks, and cluster_2 has 33 blocks. The testing method employs the silhouette coefficient, and the silhouette score testing results indicate the formation of 3 clusters (K=3) with a value of 0.61. The findings of this study include patterns, graphs, and production tables generated using the K-Means Clustering method at Pt Arta Prigel.