Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions - Hasdyna, Novia; Yafis, Balqis; Dinata, Rozzi Kesuma (UIN Sunan Kalijaga Yogyakarta, 2025)
The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to enhance the performance of the K-Means algorithm by integrating the Purity method, with a specific focus on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity K-Means. Integrating the Purity method enhances the efficiency of K-Means by reducing the number of required convergence iterations. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency.