Analisis Sentimen Terhadap Film “Dirty Vote” Pada Media Sosial X dan Youtube dengan Algoritma Naive Bayes dan SVM - Sasongko, Kukuh Hadi; Hilda, Atiqah Meutia (Forum Kerjasama Pendidikan Tinggi (FKPT), 2024)
Indonesia's presidential and vice-presidential elections in 2024 sparked widespread discussion on social media, particularly regarding Gibran's candidacy as a vice presidential candidate. The documentary Dirty Vote deepened the discussion by exposing the practice of fraud and manipulation in the election, raising public concerns about the integrity of the election. This study aims to analyze public sentiment towards the Dirty Vote film on social media YouTube and Twitter (X) using Naïve Bayes and SVM algorithms. Data was collected through crawling techniques on YouTube and Twitter (X) from February 11, 2024 to August 30, 2024. The preprocessing stages include Cleansing, Transform Cases, Tokenizing, Stopwords Removal, and Stemming. The data obtained is then classified into positive and negative sentiment categories. Model evaluation is done using Confusion Matrix which includes accuracy, precision, and recall. The results showed variations in model performance on both social media. On YouTube, Naïve Bayes algorithm achieved 81.24% accuracy, with 63.44% precision and 100.00% recall, while SVM showed 86.94% accuracy, 91.62% precision, and 65.92% recall. On Twitter (X), Naïve Bayes produced the highest accuracy of 95.13%, precision 88.86%, and recall 100.00%, while SVM recorded the same accuracy of 95.13%, with the highest precision of 99.66% and recall 87.76%. These results show that SVM is superior in precision, while Naïve Bayes has a consistently high recall. The analysis showed dissatisfaction with election integrity on almost all YouTube and Twitter (X) platforms.