Open AccessAssociation Rule Implementation Using Algorithm Apriori To Analize Fishing Pattern In Indonesia
Author(s) -
Titin Kristiana,
Sukmawati Anggraeni Putri,
Nurmalasari,
Rani Irma Handayani,
Nita Merlina,
Norma Yunita
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012072
Subject(s) - association rule learning , apriori algorithm , a priori and a posteriori , data mining , computer science , associative property , process (computing) , value (mathematics) , association (psychology) , algorithm , mathematics , machine learning , philosophy , epistemology , pure mathematics , operating system
There are more than 80 species of fish caught by fishermen in the sea of Indonesia. To find out what kinds of fish mostly caught, it is necessary to analyse the data pattern of fish being caught. The activities of searching and associating the data pattern are closely related to data mining technique that being used to discover the rules of association of items. In this associative rule method, there are two process can be used: the process of generating frequent itemset and finding associative rules. The Frequent Itemset Generation is a process to get the connection of the itemset and the value of the association based on the value of support and confidence. The algorithm used to generate the frequent itemset is Apriori Algorithm. The Apriori Algorithm has a weakness in the extraction of the appropriate feature of the used attributes. This condition causes the rules formed in large number. This research applies Apriori Algorithm based on principal component analysis to obtain more optimal rules. After the experiments using the apriori algorithm applied with the magnitude ˚ = 30, minimum Support 80% and Confidence 80%, the result of the rule formed are totally 82 rules.