Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm

This study aims to analyze the data to determine the correlation between fish catch, whether certain fish affect the other fish. Lots of natural resources in Indonesia, especially in the marine sector that can be used one of them are fisheries. Each region has the potential of marine fish species with different numbers and species, this can lead to problems that lack of information on the correlation between fish catch, whether certain fish potentially affect the catch. To overcome this problem, it is necessary to analyze the pattern of fish catch data using data mining technique. The method used is association rules with FP Growth algorithm. The method of association rules is used to analyze the data so as to produce data in the form of correlation pattern between fish catch. Thus, based on the analysis of fish data the higher the minimum support and minimum confidence used, the less frequent itemset and rules that is formed and decreases the accuracy. All rules generated in this study have a value of lift ratio of more than 1.00 so that it can be used as a reference in knowing the correlation between fish catch to optimize the fisheries results for the fishermen. General Terms Data Mining, FP Growth Algorithm, Association rules, Rapid Miner.