Muhammad Murtadha RAMADHAN* , Imas Sukaesih SITANGGANG, Fahrendi Rizky NASUTION and Abdullah GHIFARI
Parameter optimization is one of methods to improve accuracy of machine learning algorithms. This study applied the grid search method for tuning parameters in the well-known classification algorithm namely Random Forests. Random Forests was implemented on the voice gender dataset to identify gender based on the human voice’s characteristics. There are two parameters that were tuned to obtain the optimal values. Those parameters are number of variables used in building trees and number of trees that involves in the classifiers. Experimental results on voice gender dataset show that the highest accuracy of Random Forest with parameter tuning is 0.96907 which is higher than the accuracy of the model without parameter tuning (0.9675). The optimal parameter for the best classifier is number of variables is ‘sqrt’ which is square root of parameters involved in dataset and number of trees is 300. This study shows that the tuning parameter results optimal parameters for developing the best classifier using Random Forests.
Paper penuh dapat diunduh di: DPI Proceedings.