Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding

Abstract

The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree algorithm is C4.5. The C4.5 algorithm can be applied in classifying prospective recipients of educational aid funds. This study uses datasets from student data of SMK Al Fattah Kertosono. The purpose of this study is to increase the accuracy of the C4.5 algorithm by applying adaboost in classifying students who deserve education funding and not, by comparing the results before and after applying adaboost. Validation in this study uses cross validation. While the measurement of accuracy is measured by the confusion matrix. The experimental results show that there is an increase in accuracy of 7.2%. The accuracy of the application of the C4.5 algorithm reaches 91.32%. While the accuracy of the application of the C4.5 algorithm with adaboost reached 98.55%.