Application of the Naive Bayes Algorithm for Classification of Family Hope Program (FHP) Assistance Recipients
Abstract
One of the classification methods commonly used to determine the eligibility of recipients of the Familiy Hope Program (FHP) is the Naive Bayes Algorithm, also often called the Naive Bayes Classifier. This method is probability to classify data quickly and efficiently for eligibity analysis in social assistance programs. Naive Bayes is classification that uses a probabiity and statistical approach to group data. In this study, the Naive Bayes Algorithm was applied in classifying recipients of the Family Hope Program and to determine the level of accuracy, recall and precision of the Naive Bayes method. The results of this study are the accuracy, recall and precision of the Naive Bayes methode. The results of this study are the accuracy value produced by the Naive Bayes methode of 90% in the division of training and testing data 60%:40% the accurqacy value of 93% in the division of training and testing data 70%:30%, and the accuracy values of 90% ih the division of training and testing data 80%:20%.
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