PUBLIC SENTIMENT ANALYSIS TOWARDS THE DISCOURSE OF MILITARY SERVICE FOR PROBLEMATIC CHILDREN USING INDOBERT ON SOCIAL MEDIA X

Abstract

This research aims to analyze public sentiment towards the discourse of military service for problematic children using data from social media X. The approach used is deep learning based on Transformer with the IndoBERT model. The dataset was obtained through a scraping process and produced 421 data after the preprocessing and labeling stages. The research stages include text preprocessing, tokenization, sentiment labeling, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The research results show that the IndoBERT model is capable of achieving an accuracy of 68.23%. The sentiment distribution is dominated by negative sentiment (55.34%) which reflects public resistance to the policy. The contribution of this research lies in the integration of non-standard language preprocessing with IndoBERT as well as the analysis of public opinion on specific social issues