Aim: To enhance accuracy in text classification on coronavirus tweets using Novel Random Forest Classifier compared with Decision Tree. Materials and Methods: This study contains 2 groups i.e. Novel Random Forest Classifier and Decision tree algorithm. Each group consists of 10 sample sizes i.e. Novel Random Forest Classifier (Group 1=10) and Decision Tree (Group 2=10). The study parameters include alpha value 0.05, beta value 0.2, and the G power value 0.8. Results: The Novel Random Forest is (81.60%) more accurate than the Decision Tree of (80.14%) in classifying the coronavirus tweets with p<0.717. Conclusion: The Novel Random Forest model is significantly better than the Decision Tree in identifying Text Classification on Coronavirus Tweets.