Comparison of Accuracy between Naive Bayes and Decision Tree Methods for Property Tax (PBB-P2) Compliance in Tebing Tinggi City - Zulham Sitorus; Sugeng Pranoto; Sulis Sutiono; Sarifuddin (Yayasan Sinergi Multidimensi Kreatif, 2024)
This research aims to compare the accuracy of the Naïve Bayes and Decision Tree methods in predicting Land and Building Tax (PBB-P2) compliance in Tebing Tinggi city. The data used includes tax and payment determination for 2022 and 2023. The methods applied include data preprocessing, use of an inconvenience matrix for evaluation, as well as measuring accuracy with various data sharing ratios (80:20, 75:25, 70:30, 60:40, and 50:50). The research results show that the Decision Tree model consistently has much higher accuracy compared to the Naïve Bayes model, with accuracy reaching 99% at all data split ratios, while Naïve Bayes shows accuracy between 54% and 56%. The confusion matrix supports this finding by showing that the Decision Tree model has higher True Positives and True Negatives, and lower False Positives and False Negatives compared to Naïve Bayes. In conclusion, the Decision Tree method is more effective in classifying tax compliance compared to Naïve Bayes so that it is a more optimal choice for a tax compliance classification system based on the accuracy and performance obtained from this research.