Analisis Sentimen Media Sosial X Terhadap Kenaikkan PPN di Indonesia Menggunakan Algoritme Naïve Bayes dan Support Vector Machine (SVM)

Ikhsan, Ali Nur
Pungkas Subarkah
Alifah Dafa Iftinani
Alif Nur Fadilah

Infotekmesin
Vol 16 No 1 (2025): Infotekmesin: Januari 2025

One of the ways to increase state revenue is by raising the Value-Added Tax (VAT). However, implementing a VAT hike policy often elicits both positive and negative responses from the public. With the presence of social media, people can voice their opinions about government policies, including through social media platform X. This study aims to analyze public sentiment on social media X using the Naïve Bayes and Support Vector Machine (SVM) algorithms. The research compares the highest accuracy results before and after the balancing process. The dataset comprises 2,852 rows in CSV format. The findings indicate that the SVM algorithm achieves an accuracy of 98% before balancing and 97% after balancing, while Naïve Bayes achieves an accuracy of 97% before balancing and 90% after balancing. Overall, both algorithms demonstrate good and balanced performance.