TEXT CLUSTERING ANALYSIS FOR PUBLIC SENTIMENT BASED ON TWITTER OPINIONS ON E-TILANG

Abstract

Electronic Tickets (E-Tilang) have the advantage of faster service and have a very practical and fast system. Implementation of the E-Tilang system to facilitate transparency in the implementation of the ticket process or as a substitute for the ticket process on the spot. The effectiveness and efficiency of the E-Tilang system has generated various comments from the public. This study aims to determine the public's perception (sentiment) about E-Tilang. The data used in this study comes from people's tweets on Twitter about electronic tickets. This research shows that from the 524 Tweet data obtained, 252 people have positive sentiments, there are 98 people who have negative sentiments and there are people who have 174 neutral sentiments towards E-Tilang.