Implementasi Principal Component Analysis (PCA) pada K-Means untuk Klasterisasi Data Kelapa Sawit Berdasarkan Variabel Karakteristik Lingkungan

Abstract

This research aims to cluster oil palm data based on environmental characteristics using Principal Component Analysis (PCA) and the K-Means algorithm. PCA is employed to reduce the dimensionality of environmental data into simpler principal components. The reduced data is then used for clustering purposes with K-Means. Standard mutations from non-yielding to yielding oil palms are relatively rare. In other words, the development of these standards is still limited, and this study is expected to provide additional insights into the management and development of such standards. The research results indicate that the combination of PCA and KMeans is effective in identifying significant clusters in oil palm data based on environmental variables, which can offer valuable insights for more efficient oil palm land management. This research contributes significantly to the field of data analysis and environmental management in the agricultural sector.