Sentiment Classification Twitter of LRT, MRT, and Transjakarta Transportation using Support Vector Machine

Abstract

The use of public transportation facilities such as MRT, LRT, and Transjakarta by the people of the capital city is an alternative in reducing congestion. However, the services provided by MRT, LRT and Transjakarta transportation service providers vary, such as positive and negative responses. The effectiveness of public transportation facilities can be seen through public opinion. This study aims to classify positive and negative tweet sentiments sourced from Twitter data using the Support Vector Machine (SVM) algorithm. The results of this study indicate that the Support Vector Machine method is able to classify positive and negative sentiment text with an accuracy result of 91.89% with 79.2% positive sentiment and 20.8% negative sentiment.