Modeling of Naïve Bayes and Decision Tree Algorithms to Analyze Sentiment Related to Jaklingko Public Transportation on Social Media X (Twitter)

Abstract

Technological developments have changed the way people access information and share opinions through the internet and social media such as X (Twitter). Public sentiment is now crucial in evaluating public services, especially in the era of advanced information technology. As a metropolitan city, public transportation in DKI Jakarta plays an important role in economic, business and government activities. The Jaklingko initiative launched by the DKI Jakarta Provincial Government aims to provide efficient and affordable public transportation. This research implements and compares Naive Bayes and Decision Tree classification methods to perform sentiment analysis of twitter users opinions regarding Jaklingko into positive and negative categories. Data was collected by crawling using tweet_harvest which obtained 6001 tweets about Jaklingko, then text preprocessing was carried out. Word weighting is done using the TF-IDF method to give value to each term in the document, and sentiment labeling is done using the Vader Lexicon library. The data is divided into training and testing data with a ratio of 80%:20% for the classification process. Evaluation of the method is done using confusion matrix. The results showed that the accuracy of Naive Bayes reached 84.9% and Decision Tree reached 84.2%. The wordcloud visualization depicts negative words including vehicle stoppage, bad driver attitude, and envy from people in other cities. Meanwhile, positive words included free system, useful programs, and user convenience. This research provides an in-depth understanding of public opinion towards Jaklingko, with potential implications for improving public transportation services in Jakarta.