Binti Mamluatul Karomah
Universitas Nahdlatul Ulama Indonesia, Jakarta

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Journal : Journal of System and Computer Engineering

Sentiment Analysis in Indonesian’s Presidential Election 2024 Using Transfomer (Distilbert-Base-Uncased) Aljabar, Andi; Karomah, Binti Mamluatul; Tarisafitri, Nahla; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1867

Abstract

Utilizing a transformer-based natural language processing model called DistilBERT-base-uncased, this study investigates the use of sentiment analysis in relation to Indonesia's 2024 presidential election. Particularly during political events, sentiment analysis is a potent tool for gaining insight into public opinion. The program divides public posts' sentiment into positive and negative categories by examining social media data (twitter). In order to assure consistency and correctness, the dataset used in the research has been carefully selected. DistilBERT is then used to train the model. The result shows from 19920 row of data only 4.47% of Indonesia’s citizen left positive comment.