Sentiment Analysis of the Policy of Providing Contraceptive Provision Policy for Teenagers in PP Number 28 Year 2024 with Naïve Bayes Classifier Method on Twitter - Ira Zulfa; Eliyin Eliyin; Firmansyah Firmansyah; Zikri Syah Dermawan (Asosiasi Riset Teknik Elektro dan Infomatika Indonesia, 2025)
The plan to offer birth control to teenagers, outlined in Government Regulation (PP) No. 28 of 2024, has sparked different responses in the public, especially on social media sites like Twitter. This research intends to look into how people feel about this plan by using the Naïve Bayes Classifier technique. Information was gathered from Twitter by using data collection methods with the snscrape tool and the Python coding language. A total of 1,000 tweets related to the topic of the policy were gathered and went through initial processing steps like cleaning, breaking into words, changing cases, and removing common words. The Naïve Bayes Classifier technique was employed to sort the public's feelings into three groups: positive, negative, and neutral. The findings showed that half of the tweets (50%) had a negative view on the policy, while 35% had a positive outlook, and 15% were neutral. The accuracy of the method used was 78%, with a precision of 74%, a recall of 79%, and an F1-score of 76%. The findings from this research offer a summary of how the public feels about the birth control policy for teenagers, which can help the government assess and create policies that better meet the community's needs and worries. Additionally, this research highlights how well the Naïve Bayes Classifier method works for analyzing sentiments on social media, even though there are some challenges when it comes to understanding language subtleties like sarcasm.