Increasing Accuracy of C4.5 Algorithm Using Information Gain Ratio and Adaboost for Classification of Chronic Kidney Disease - Aprilia Lestari; Alamsyah (Journal of Soft Computing Exploration, 2020)
Data information that has been available is very much and will require a very long time to process large amounts of information data. Therefore, data mining is used to process large amounts of data. Data mining methods can be used to classify patient diseases, one of them is chronic kidney disease. This research used the classification tree method classification with the C4.5 algorithm. In the pre-processing process, a feature selection was applied to reduce attributes that did not increase the results of classification accuracy. The feature selection used the gain ratio. The Ensemble method used adaboost, which well known as boosting. The datasets used by Chronic Kidney Dataset (CKD) were obtained from the UCI repository of learning machine. The purpose of this research was applying the information gain ratio and adaboost ensemble to the chronic kidney disease dataset using the C4.5 algorithm and finding out the results of the accuracy of the C4.5 algorithm based on information gain ratio and adaboost ensemble. The results obtained for the default iteration in adaboost which was 50 iterations. The accuracy of C4.5 stand-alone was obtained 96.66%. The accuracy for C4.5 using information gain ratio was obtained 97.5%, while C4.5 method using information gain ratio and adaboost was obtained 98.33%.