SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC

Abstract

Sentiment analysis is a data text analysis that classifies data into positive and negative sentiments. This study aims to obtain the results of sentiment classification related to Merdeka Belajar Kampus Merdeka policy on Twitter using a support vector machine algorithm with Word2Vec feature extraction. Support Vector Machine is a classification algorithm that separates data classes using the optimum hyperplane. Text data in sentiment analysis must change its numerical form by performing feature extraction. In this study, the feature extraction used is Word2Vec which represents words in vector form. Data in this study are tweets with the keyword "Kampus Merdeka" uploaded on Twitter as many as 10000 tweets. After preprocessing text data, data used to analyze sentiment was 1579 tweets. Sentiment classification resulted in a classification model accuracy of 89.87%, a precision of 91.20%, a recall of 84.44%, and F-Measure of 87.68%. Classification sentiment using a support vector machine with Word2Vec feature extraction in this study produces a good model.