Optimization of Apriori Algorithm in Association Model to the Home Surgery Assistance Selection

Abstract

Data mining is the process of extracting and discovering patterns in large data sets. Association models in data mining, especially using a priori algorithms, are a useful approach for determining correlations and relationship patterns between itemsets. The problem of inaccurate data determined manually in determining the attribute criteria for data on candidates for selecting house renovation assistance in Nelayan Indah Subdistrict is one of the fundamental problems that the community continues to face. By applying the association method, this research aims to analyze the itemset data calculation system for each specified home renovation assistance data attribute criteria and using an a priori algorithm method that can be tested based on the help of the rapidminer tool by determining the minimum support value and minimum confidence value. In this research, the results of calculation testing using the a priori algorithm method from the provisions of the support values and confidence values obtained are then applied in system testing and implemented into a website-based application system with the provisions of the input data item criteria and producing output from the a priori calculation process.