COMPARISON OF ACCURACY OF VARIOUS TEXT CLASSIFICATION METHODS IN SENTIMENT ANALYSIS OF E-STAMPS AT X

In the rapidly evolving digital era, technological innovations are applied in various fields, including law and administration, to improve the efficiency and effectiveness of processes. One of the latest innovations in Indonesia is the implementation of e-metals, which is designed to facilitate legal and secure electronic transactions, and meet the needs of a society that is increasingly dependent on digital technology. Although e-stamps aim to improve efficiency and security in transactions, there are still various perceptions from the public that reflect their views and experiences regarding the implementation of this technology. In this case, sentiment analysis is an effective method to evaluate public opinion generated from text data, such as user reviews and comments on social media. This research aims to analyze the sentiment towards e-metallocations in X app, using text classification methods to separate positive and negative sentiments. After collecting 3282 datasets and performing preprocessing that includes case folding, data cleaning, tokenizing, and stemming, the evaluation results show that the Naive Bayes (GNB) model achieves 96.65% accuracy on training data and 95.28% on testing data. On the other hand, the Support Vector Machine (SVM) model recorded an accuracy of 98.32% on training data and 96.80% on testing data. Meanwhile, the Random Forest model showed a perfect accuracy of 100% on training data and 99.09% on testing data, making it the highest performing model among the three methods tested.