ANALISIS SENTIMEN PUBLIK TERHADAP KABINET MERAH PUTIH PADA APLIKASI X DENGAN METODE BIDIRECTIONAL ENCODER REPRESENTATION FROM TRANSFORMER

Abstract

The X application has become a platform for Indonesians to express their opinions, including those related to the Kabinet Merah Putih. This study aims to analyze public sentiment on the X application regarding the Kabinet Merah Putih using the IndoBERT method. A total of 1944 tweets were collected from the X platform using the keyword “Kabinet Merah Putih”, then processed through preprocessing stages including cleaning, normalization, case folding, and stopword removal, automatic labeling with TextBlob, and data division for model training and testing. The finetuned IndoBERT model was used to classify sentiment into positive, negative, and neutral. The results of the study show that public sentiment is dominated by positive and neutral sentiments, while IndoBERT's performance shows good accuracy in sentiment classification, with a model accuracy value of 85.96%, precision of 75%, recall of 74%, and F1-Score of 74%. This study provides an objective picture of public perception of the Kabinet Merah Putih on the X application and is expected to serve as evaluation material for the government.