Penggerombolan Provinsi di Indonesia Berdasarkan Produktivitas Tanaman Pangan Tahun 2005-2015 Menggunakan Metode K-Error

Abstract

Clustering analysis is a multivariate analysis that’s aim for gruping the observasion objects to some groups. The clusters have low similarity between the clusters and high similarity in same cluster. Classic grouping analysis have a weakness that doesn’t insert measurement error information that related with data. Clustering analysis with K-Error method is expanded for solusing solving the measurement error data problem in classic grouping analysis. The research is aim for clustering the provinces in Indonesia using K-Error and K-Means method based on crops productivity. K-Error method produces better clusters than KMeans. K-Error method formed 7 clusters. Cluster 5 consist of provinces with highest productivity almost at all crops. Cluster 2 and 3 have low productivity for partial crops.