Batik price estimation system for textile production

Batik brings considerable amount of revenue to textile industry in South-East Asia. Lack of supporting information which is price estimation suggested by the information system, leads the business to loose in competition on AEC market. There are a number of factors that affect Batik industry. These complex factors have to be analyzed and derived as a simple suggestion for entrepreneurs. In this paper, two-stage clustering is proposed as the decision support system to increase productivity by suggesting reasonable materials for upcoming product. The system is trained with the sets of data that composed with thirty nine factors to let the system learn to select the most suitable materials as solution of reasonable investment for entrepreneur according to their given price as input. The result shows that this proposed technique perform well with 84.99% of accuracy on k=8.