The Node Selection Method for Split Attribute in C4.5 Algorithm Using the Coefficient of Determination Values for Multivariate Data Set - Muhsi Muhsi; Suprapto Suprapto; Rofiuddin Rofiuddin (Jurnal Penelitian Pendidikan IPA, 2023)
The split attribute in the decision tree algorithm, especially C4.5, has an important influence in producing a decision tree performance that has high predictive performance. This study aims to perform an attribute split in the C4.5 algorithm using the value of the termination coefficient (R2/R Square) which is combined with the aim of increasing the performance of the model performance produced by the C4.5 algorithm itself. The data used in this research are public datasets and private datasets. This study combines the C4.5 algorithm developed by Quinlan. The results in this study indicate that the use of the R2 value in the C4.5 algorithm has good performance in terms of accuracy and recall because three of the four datasets used have a higher value than the C4.5 algorithm without R2. Whereas in the aspect of precision, it has quite good performance because only two datasets have a higher value than the performance results of the algorithm without R2.