Title: The implementation of sentiment analysis on Indonesian sexual violation bill using Naïve Bayes algorithm
Abstract: The cases of women's sexual violations in Indonesia annually increase, and due to this fact, reforming the sexual violation bill for protecting Indonesian women is critical. The Indonesian government is currently launching a draft of the Indonesian sexual violation bill to refine the previous sexual violation bill. This study focuses to identify the sentiment of Twitter users in Indonesia about women's sexual violence bill using the Naïve Bayes algorithm. The data is taken from Twitter from October to December 2020 via Twitter API. The data collected is 1132 tweets divided into 90% of training data and 10% of testing data using Python. The several results of this study are, the accuracy score is 96%, the precision score is 100%, the recall score is 35% and the F1 score is 0,67. It means that 35% of the Indonesian Twitter users support the Indonesian sexual violation bill draft.