Node Selection Method for Split Attribute in C4.5 Algorithm Using the Coefficient of Determination Values for Multivariate Data Set

The split attribute in the decision tree algorithm, especially C4.5, has an important influence producing a performance that high predictive performance. This study aims to perform C4.5 algorithm using value of termination coefficient (R2/R Square) which is combined with aim increasing model produced by itself. data used this research are public datasets and private datasets. combines developed Quinlan. results indicate use R2 good terms accuracy recall because three four have higher than without R2. Whereas aspect precision, it quite only two