Sentiment Analysis of Transjakarta Based on Twitter using Convolutional Neural Network

摘要

TransJakarta is one of the methods to reduce congestion in Jakarta. However, the number of TransJakarta users compared to number of private vehicle users is very small, only 24% of the total population in Jakarta. The purpose of this research is to know public opinions about TransJakarta whether positive or negative by doing sentiment analysis about TransJakarta based on the opinion of Twitter, as Twitter is one of media to express its many users to express their opinions about an individual or an instance.Data is retrieved from Twitter using the R-Studio application by utilizing the “TwitteR” library, then pre-processing and stored in a database. Next step is labelling the data using Sengon Lexicon and will be trained and tested using the Convolutional Neural Network algorithm. There are three CNN architectural models to be tested, namely VGG, ResNet, and GoogleNet. The designed VGG consists of 16 layers, ResNet 34 layers, and GoogleNet 22 layers. After the data are trained and tested, the results will be evaluated using Confusion Matrix to get the best F-Score. The results showed that among the three architectural models that were tested, the Resnet 34 layers architecture model gave the best F-Score of 98.11%, better compared to VGG which had the highest F-Score value of 96.74% and GoogleNet of 96.80%.