PENGARUH EKSTRAKSI FITUR TERHADAP ANALISIS SENTIMENT PADA DATA REVIEW PELAYANAN INDIHOME BERBASIS NAÏVE BAYES

Along with the progress of the times, the mass media used to seek information by the public is also progressing rapidly, especially in internet technology. One part of internet technology that is widely used by the community is the use of social media, for example the social media twitter. Twitter social media can be used to convey a user's feelings or opinions aimed at the general public. In this study, sentiment analysis was carried out regarding public responses or reviews about IndiHome services on Twitter social media. This study uses a comparison of TF-IDF and Word2Vec feature extraction, and the classification method used is the nave Bayes classifier. The accuracy results obtained in this study were 96% using the Tf-Idf feature extraction and testing was carried out using an unseen data test that was selected randomly resulting in an accuracy of 92%. While the accuracy value obtained by using the Word2Vec feature extraction is 60% by testing using unseen test data selected randomly resulting in an accuracy value of 44%.