Aim: To enhance the accuracy in classifying fake currency detection using Novel Random Forest Algorithm and Decision Tree Classifier. Materials and Methods: This study contains 2 groups such as Novel Random Forest Algorithm and Decision Tree Classifier. Each group consists of a sample size of 10 and the study parameters include alpha value 0.05, beta value 0.2. SPSS was used for predicting significance value of the dataset considering G-Power value 80%. Their accuracies are compared with each other using different sample sizes. Results and Discussions: The Novel Random Forest Algorithm with accuracy 80.5%, is more accurate than the Decision Tree Classifier with accuracy value 47.5% in classifying the fake currency notes with significance value 0.001 (p<0.05). Conclusion: The Random Forest Algorithm is significantly better than the Decision tree classifier in identifying fake notes. It can be also considered as a better option for the classification of fake currency.