Analisa Sentimen Pengguna Transportasi Jakarta Terhadap Transjakarta Menggunakan Metode Naives Bayes dan K-Nearest Neighbor

Abstract

Social media used in communicating that is very popular in Indonesia. One of the most popular is Twitter. Twitter is a social media site where people can share information publicly. This information can be processed to make sentiment analysis. This research attempts to create a system that can detect positive or negative sentiments in public information. The method used for this sentiment classification is the comparison method of Naive Bayes Classifier and K-Nearest Neighbor Classifier using TF-IDF weighting. The input to this system is in the form of tweet data for Transjakarta, while the output of this system is in the form of visualization of positive and negative sentiment data using Streamlit which is a library from python. Based on testing the accuracy of the Naive Bayes approach for sentiment analysis of Twitter data related to the use of Transjakarta transportation is 61.1%, and the accuracy of the K-Nearest Neighbor method is 75.7%. For the two methods used in determining the level of accuracy, it can be concluded that the K-nearest-neighbor method produces better accuracy.