Sentiment Analysis of the Free Lunch Program by Prabowo–Gibran Using the Naive Bayes Classifier - Dewa Ayu Putri Wulandari; Gede Wirya Wardhana; I Dewa Gede Puja Wirata Sindu (Jurnal Krisnadana, 2025)
This study aims to conduct sentiment analysis on the “Prabowo-Gibran free lunch” program which is widely discussed on social media Twitter. In this study, two algorithmic approaches were used, namely the Naive Bayes Classifier. The research stages include the data crawling process using tweet-harvest, text preprocessing (cleaning, normalization, tokenization, to stemming), dataset labeling using a lexicon approach validated by linguists, training and test data division, model training, performance evaluation, and visualization of results. A total of 2,191 tweets were successfully analyzed, with a sentiment distribution of 52.99% positive, 25.83% negative, and 21.18% neutral. Evaluation resultsThe Naive Bayes model generally shows a fairly high precision value on each test data, ranging from 81% to 82% with an accuracy value of 73.64%. This shows that the model is able to classify correctly for the dominant class, namely the Positive class.