KLASIFIKASI SENTIMEN PERGELARAN MOTOGP DI INDONESIA MENGGUNAKAN ALGORITMA CORRELATED NAÏVE BAYES CLASIFIER - RIDWAN INDRANSYAH; Yulison Herry Chrisnanto; Puspita Nurul Sabrina (Universitas Majalengka, 2022)
Knowing the public's sentiment towards the international MotoGP event which has been held in Indonesia in 2022 is very necessary because the role of the community is very influential in the implementation and public interest in visiting an international event is still few and difficult because the information is still limited. Tweets, comments, reviews, and opinions of people using social media play an important role in determining whether a particular population is satisfied with products, performances, and services. The method used in this study is the Correlated Naïve Bayes Classifier (CNBC). The Correlated Naive Bayes Classifier (CNBC) method recalculates the correlation value for each attribute of the dataset to that class. There are several processes carried out in this study including data acquisition, data labeling, data preprocessing, feature extraction, classifying data using the Correlated Naive Bayes Classifier (CNBC) method, visualizing data, and finally evaluating the results. This study resulted in an accuracy of 82%.