Implementation of Cluster Analysis and Artificial Neural Networks as an Alternative for Klassen Typology and LQ: Case of Coconut

Abstract

Due to the campaign supported by manufacturers of soy-oil in the United States which stated that consuming coconut oil might cause heart disease, coconuts' popularity started to decline. As a result, the coconut was neglected by farmers and productivity was declining. Yet, recent study showed the opposite results. Coconut was good for health. As a result, the demand for coconut products increased. Indonesia, as the world's largest coconut producers cannot maximize these opportunities due to aging coconut which have been neglected for many years. Coconut products may improve the income of farmers and encourage sustainable agriculture as well as diversify farmers' income. Some simple methods, such as Klassen Typology or Location Quotient can be used to classify the potential area to be developed or rejuvenated. Nevertheless, these approaches are not able to generalize and not suitable to be implemented for cases with large data. This research tried to use cluster analysis such as dendrogram, Principle Component Analysis and Artificial Neural Network for classification. The results show that the dendrogram provided good results, whilst the Principle Component Analysis and Artificial Neural Networks required more data for better results.