Implementation of Large Language Models in Sentiment Analysis for Presidential Candidate Elections

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Published Aug 31, 2024
https://doi.org/10.24252/jagti.v4i2.83
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Vol. 4 No. 2 (2024): March - August

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A.ALI AKBAR KHAERUN
Universitas Islam Negeri Alauddin Makassar
Nur Afif
Universitas Islam Negeri Alauddin Makassar
Mustikasari Mustikasari
Universitas Islam Negeri Alauddin Makassar

Abstract

The presidential candidate election in Indonesia is a hot topic on social media, especially Twitter. This study analyzes public sentiment regarding the 2024 presidential candidate election using the IndoBERT model, which is specifically designed for the Indonesian language, on a dataset of 8,442 tweets. This research follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data was collected through crawling with keywords related to the election, followed by preprocessing and manual labeling before being processed by the model. The results show that IndoBERT achieved an accuracy of 98%, with precision, recall, and F1-score also at 98% at the 10th epoch. Batch size evaluation indicated that a batch size of 4 yielded the best performance. This model is effective in classifying sentiment related to the 2024 presidential candidate election and serves as a useful tool for understanding public opinion.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
[1]
A. A. KHAERUN, N. . Afif, and M. Mustikasari, “Implementation of Large Language Models in Sentiment Analysis for Presidential Candidate Elections”, Jagti, vol. 4, no. 2, pp. 23-28, Aug. 2024.

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