Comparison of Expectation-Maximization (EM) Algorithm and Kmeans for District/City Clustering in West Sumatera Province Based on Breadfruit Production

Abstract

Breadfruit (Artocarpus altilis) is an important food source that is highly nutritious and plays a strategic role in West Sumatra Province. However, challenges such as pests, diseases and marketing constraints affect its cultivation and productivity. This study employed K-means and expectation-maximisation (EM) clustering methods to categorise regions according to their breadfruit cultivation characteristics. The elbow method identified three optimal clusters for K-means and seven for EM. Evaluating the quality of the clusters using the silhouette coefficient produced values of 0.47 and 0.37 for EM and K-Means respectively, indicating that EM produced tighter, more distinct clusters. These results suggest that EM is a more effective method for describing the variation in breadfruit production in West Sumatra. With this in mind, the research is expected to inform strategic decision-making aimed at increasing the productivity and added value of breadfruit crops in the area..