Text Mining untuk Sentimen Analisis dengan Metode Naïve Bayes, SMOTE, N-Gram dan AdaBoost Pada Twitter CommuterLine

Abstract

In the current era, the development of information technology and social media is growing rapidly so that it can provide updated information and various kinds of public opinion. Many internet users in Indonesia use social media for various purposes, such as seeking information and expressing opinions through social media. One of the social media that is widely used by internet users in Indonesia is Twitter. Twitter users can provide information in the form of comments, criticisms, or suggestions for Comutterline services more quickly and easily. Sentiment analysis can help provide an overview of public perception by grouping opinions into positive and negative categories for Commuterline services. Conducting sentiment analysis based on comments or Tweets from the community on Twitter Commuterline to determine the performance of the Naïve Bayes Classifier algorithm, Synthetic Minority Over-sampling Technique (SMOTE), AdaBoost, and N-Gram so that machine learning implementation can help identify public opinion conveyed through Twitter automatically into positive and negative categories. The use of the Naïve Bayes Classifier, Synthetic Minority Over-sampling Technique (SMOTE), AdaBoost, and N-Gram methods which are considered better to generate predictions on tweets sent by CommuterLine users