The hard c-means algorithm for clustering Indonesian plantation commodity based on metabolites composition. (October 2019)

• Record Type: Journal Article
• Authors: Rustam, ; Gunawan, A Y; Kresnowati, M T A P
• Is Part Of: Journal of physics. Volume 1315(2019)
• Publication Date: 2019-10
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1315/1/012085
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Indonesia is the second largest biodiversity country in the world. Indonesia has a variety of top agricultural and plantation commodities, whereas each commodity has premium products which are normally related to its specific character/flavour and are generally associated with the producing origins. Specific character/flavour is represented by composition of metabolites each origin. To find out the specific character/flavour in various Indonesian plantation commodities, clustering is needed. In this paper, we perform clustering on an Indonesian plantation commodity based on their metabolites composition. Metabolite compositions of samples of some origins of the commodity are clustered using the Hard C-Means algorithm with the Xie-Beni index as the cluster validity. Our present results confirm that each origin has a unique characteristic and belongs to a separate cluster.