| dc.description.abstract | With the rise of social media platforms, the spread of fake news has become a significant concern. During the 2024 presidential election is dominated with novice voters, who are exposed to a lot of news from social media. As first-time voters, they get a lot of information and news exposure mainly from social media. This is also exacerbated by the fact that influencers are used to lead opinions. This research tries to measure the resilience of novice voters in dealing with hoax news compared with Naïve Bayes classifier to assessing the news. The purpose of this research is so that novice voters aware and are not easily polarized to prevent national disintegration due to disinformation and hoax news. Subsequently, this research also tries to develop a database of content and categories for hoax news from beginner voter data with a classification model. Data collection was carried out offline and online with interviews and questionnaires conducted with a total of 283 respondents from two private universities in East Java and came from various study programs. From the data, a classification approach using the naïve Bayes method was also built to help recommend a category whether this news is a hoax or news that can be verified. From the results of this study, it can also be concluded that the classification model with Naïve Bayes has a very good accuracy of up to 90.303% capable of categorizing a news story whether it is a hoax, dubious news, or valid news. In contrast, this study shows that the average accuracy of first-time voters is only 29.68%, which means that they are very vulnerable to hoax news, due to the many perceptions and assumptions in public comments that make views biased. | en_US |
