The Clustering of the Aquaculture Fisheries Companies in Indonesia Using the K-Prototypes and Two Step Cluster (TSC) Algorithm
Keywords:
Cluster, Fisheries, K-Prototypes, Mixed Data, Two Step ClusterAbstract
Background: Fisheries subsector has an important role in the Indonesian economy, especially for the aquaculture fisheries companies. Each aquaculture fisheries companies has its own characteristics like in terms of technical, financial, staffing, or input and output structures. It is necessary to clustering 258 aquaculture fisheries companies to make it easier to identify the characteristics of these different companies based on the characteristics of their cluster. One of the method that can be used to grouping objects is cluster analysis. On this study, the clustering process was using the K-Prototypes and Two Step Cluster (TSC) algorithm because the data that used in this study was the mixed data type (13 numerical and 8 categorical variables). Then this study would choose the best algorithm by the smallest ratio between the standard deviation within the cluster (SW) and the standard deviation between cluster (SB). The smallest ratio means that the diversity within clusters is quite homogeneous, while the diversity between clusters is heterogeneous. Based on the comparison of the ratio between SW and SB from the k-prototypes and the TSC algorithm, the k-prototypes algorithm with 6 clusters was the best algorithm for clustering the aquaculture fisheries companies in Indonesia. The result showed that the cluster 5 was the best cluster and the cluster 6 was the worst cluster related to the condition of the aquaculture fisheries companies in Indonesia. Cluster 5 which is characterized by most of the central companies in the form of PT and do the enlargement of sea water fish in fishpond and has a high numerical variable value. Cluster 6 which is characterized by most of the central companies in the form of PT and CV and do the hatchery of land water fish in water tubs and has the lowest value compared to other clusters.
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