Ensemble Cluster Method For Clustering Cabbage Production In East Java - Maghfiro, Maulidya; Wardhani, Ni Wayan Surya; Iriany, Atiek (Inferensi, 2024)
Cluster analysis is a multivariate analysis method classified under interdependence methods, where explanatory variables are not differentiated from response variables. The methods used include hierarchical cluster analysis, such as agglomerative and divisive, and non-hierarchical methods such as Self Organizing Maps (SOM) based on Artificial Neural Networks (ANN). Various cluster analysis methods often yield diverse solutions, making it challenging to determine the optimal solution. Therefore, the ensemble cluster method is employed to combine various clustering solutions without considering the initial data characteristics with providing better results. One case study of clustering is the grouping of cabbage production. East Java Province has become the third-highest cabbage-producing province in Indonesia with a production of 210,454 tons. Clustering of cabbage-producing regencies/cities was conducted to optimize production and identify areas that have not yet reached their maximum potential. This study compares five clustering methods which are hierarchical analysis (complete linkage, single linkage, average linkage), Self-Organizing Map (SOM), and Ensemble Cluster. The quality of clustering was evaluated using the Silhouette Coefficient (SC), Dunn Index (DI), and Connectivity Index (CI). The results indicate that the Ensemble Cluster method showed the best performance, with an SC value of 0.9124, a DI value of 1.3734, and a CI value of 2.9290, indicating excellent cluster separation. Therefore, the ensemble cluster method is recommended as the best clustering method in this study.