Analisis Sentimen Terhadap Kebijakan Pemerintah Tentang Larangan Mudik Hari Raya Idulfitri di Indonesia Tahun 2021 Menggunkan Metode Naïve Bayes

Abstract

Social media as a place to access and disseminate information has grown very rapidly, one of which is Twitter. Twitter, as a place for information flow, is a rich source for seeking public opinion and sentiment analysis. Twitter in this study was used as a source to obtain data about the 2021 homecoming in Indonesia. The purpose of this study is to determine public satisfaction with government policies regarding the ban on going home in Indonesia in 2021. The data to be processed is Indonesian-language tweets, the keywords are #mudik and #diarangmudik, the length of data collection is 1 week, with lots of data generated as many as 1000. Sentiment analysis in this study using the Naïve Bayes Classification method. The steps in this study are first crawling Twitter data which is then stored in csv format, second preprocessing which consists of tokenizer, case folding, cleansing and stop removal, third Naive Bayes classification which will be carried out after going through the Pre-processing stage, where the results of the classification tweets tend to be positive or negative or neutral. The results of this study obtained an accuracy of 56.52% with each positive sentiment value of 62.28%, negative sentiment as much as 46.72% and neutral sentiment as much as 66.50%.