PRINCIPAL COMPONENT ANALYSIS UNTUK ANALISA POLA TANGKAPAN IKAN DI INDONESIA
Abstract
Different kinds of fish in Indonesia is very much known to exist more than 80 species of fish caught in the waters of Indonesia. To find out which type of fish caught necessary analysis of the data pattern catches so as to know what kind of fish are caught. Search pattern or associative relationships of large-scale data that are closely related to data mining. Analysis of the association or the association rule mining is a data mining technique to discover the rules of associative between a combination of items. In the association rule method, there are two processes, namely the process of generating Frequent Itemset and trenching association rules. Frequent Itemset Generation is a process to get itemset interconnected and has a value of association based on the value of support and confidence. The algorithm used to generate the frequent itemset is Apriori Algorithm. Apriori algorithm has a weakness in the appropriate feature extraction that is used to attribute causing rule that formed a research a lot in based applying apriori algorithm principal component analysis to obtain a more optimal rule. After experiments using apriori algorithm with a magnitude Φ = 30, min Support 80% and 80% Confidence min rule formed results totaled 82 rules. While the second experiment was done by using an algorithm based on principal component analysis prior the magnitude Φ = 30, min Support 80% and 80% Confidence min formed results amounted to 12 rules to fully lift the ratio of 1
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