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KLASIFIKASI SENTIMEN NETIZEN X MENGGUNAKAN METODE NAIVE BAYES DAN LINEAR DISCRIMINANT ANALYSIS (LDA) (STUDI KASUS: SENTIMEN NETIZEN TERKAIT AKSI DEMONSTRASI PADA TANGGAL 1 AGUSTUS - 30 SEPTEMBER 2025)
Demonstrations are a form of public political participation that not only take place in physical spaces but also trigger the formation of public opinion on social media. Netizen’s opinions regarding demonstration movements are generally expressed through comments or posts on the social media platform X, containing various sentiments. This study aims to compare the performance of the Naïve Bayes and Linear Discriminant Analysis (LDA) methods in sentiment classification related to the DPR RI demonstration movement on social media X. The dataset was obtained through a data scraping technique on social media X within the period of August 1 to September 30, 2025, resulting in 8.702 data entries. The sentiment data were classified into three categories: positive, negative, and neutral. The research stages included preprocessing, TF-IDF weighting, and sentiment classification using the Naïve Bayes and LDA methods. Model evaluation was conducted using a confusion matrix with accuracy, precision, and F1-score metrics. The results showed that the LDA method achieved better performance than Naïve Bayes, with an accuracy of 79,21%, while Naïve Bayes obtained an accuracy of 74,01%. These findings indicate that the LDA method is more effective in classifying sentiment in unstructured and high-dimensional socal media text data.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
KLASIFIKASI SENTIMEN NETIZEN X MENGGUNAKAN METODE NAIVE BAYES DAN LINEAR DISCRIMINANT ANALYSIS (LDA) (STUDI KASUS: SENTIMEN NETIZEN TERKAIT AKSI DEMONSTRASI PADA TANGGAL 1 AGUSTUS - 30 SEPTEMBER 2025) (Text)
KLASIFIKASI SENTIMEN NETIZEN X MENGGUNAKAN METODE NAIVE BAYES DAN LINEAR DISCRIMINANT ANALYSIS (LDA) (STUDI KASUS: SENTIMEN NETIZEN TERKAIT AKSI DEMONSTRASI PADA TANGGAL 1 AGUSTUS - 30 SEPTEMBER 2025) (Other)
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