Perkembangan platform digital seperti YouTube telah menciptakan ruang publik baru untuk diskusi politik, dimana kolom komentar menjadi cerminan langsung tanggapan masyarakat terhadap konten politik. Penelitian ini menganalisis sentimen publik terhadap video "Presiden Prabowo Menjawab" di YouTube menggunakan algoritma Multinomial Naïve Bayes yang dioptimasi dengan seleksi fitur Chi-Square dan teknik SMOTE untuk penanganan ketidakseimbangan data. Data diperoleh melalui web scraping sebanyak 4.649 komentar dari tiga channel YouTube terkemuka. Preprocessing teks dilakukan melalui cleaning, case folding, normalisasi, tokenizing, stopword removal, dan stemming. Hasil penelitian menunjukkan bahwa model mencapai akurasi sebesar 67% dengan precision kelas positif 72% dan recall kelas negatif 81%. Hasil analisis mengungkapkan bahwa model lebih efektif dalam mengidentifikasi komentar negatif dibandingkan positif, yang merefleksikan karakteristik unik pola linguistik dalam diskusi politik di platform digital. Penelitian ini memberikan kontribusi dalam pengembangan sistem analisis sentimen otomatis untuk konten politik serta menjadi dasar untuk pemantauan opini publik di era digital.


Deskripsi Lain

The development of digital platforms such YouTube has created a new public space for political discussion, where the comments section directly reflects public responses to political content. This study analyzes public sentiment toward the YouTube video "President Prabowo Answers" using the Multinomial Naïve Bayes algorithm optimized with Chi-Square feature selection and the SMOTE technique to address data imbalance. Data were obtained through web scraping of 4,649 comments from three prominent YouTube channels. Text preprocessing included cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. The results showed that the model achieved an accuracy of 67%, with a precision of 72% for the positive class and a recall of 81% for the negative class. The analysis revealed that the model was more effective in identifying negative comments than positive ones, reflecting the unique characteristics of linguistic patterns in political discussions on digital platforms. This research contributes to the development of an automated sentiment analysis system for political content and provides a foundation for monitoring public opinion in the digital era.