Analisis Sentimen Fenomena FoMO Gaya Hidup Sehat Menggunakan Metode Naïve Bayes Classifier

Abstract

The development of social media has given rise to the Fear of Missing Out (FoMO) phenomenon which can affect healthy lifestyles among the community, especially the younger generation. This study aims to conduct a sentiment analysis of the FoMO phenomenon related to a healthy lifestyle using the Naïve Bayes Classifier method. Data were collected from Twitter social media using a crawling technique with certain keywords in the period from January 2024 to June 2025. The data was then processed through the preprocessing stage, sentiment labeling, data balancing using SMOTE, and feature weighting using TF-IDF. The model was trained using 2,364 training data and tested on 591 data. The evaluation results showed that the model achieved an accuracy of 84.09%, precision 85.66%, recall 84.09%, and F1-score 83.57%. Validation using 5-Fold Cross Validation also showed stable model performance with an average accuracy of 84.50%. This study proves that Naïve Bayes is effective in analyzing social media sentiment towards the FoMO phenomenon, with potential for development through dataset expansion and exploration of other algorithms