To enhance the accuracy in classifying fake currency detection using Novel Random Forest Algorithm and Logistic Regression Classifier. This study contains 2 groups such as Novel Random Forest Algorithm and Logistic Regression Classifier. Each group consists of a sample size of 10 and the study parameters include alpha value 0.05, beta value 0.2. The SPSS was used for predicting significance value of the dataset considering G-Power value 80%. The Novel Random Forest Algorithm is 65.55% more accurate than the Logistic Regression Classifier of 35.5% in classifying the fake currency notes with significance value p= 1.000. The Random forest algorithm is significantly better than the Logistic regression classifier in identifying fake notes. It can be also considered as a better option for the classification model of fake currency.