Sentiment Analysis of Indonesian Tweets on AI Impact: A Comparison between Random Forest and SVM

DOI

10.2991/978-94-6463-878-3_57How to use a DOI?

Keywords

AI Public Opinion; Indonesian Tweet Classification; Machine Learning Comparison; Random Forest; Sentiment Analysis; Support Vector Machine

Abstract

The rapid development of Artificial Intelligence (AI) has sparked diverse public opinions, making sentiment analysis a crucial tool for understanding public perception. This study conducts a comparative analysis of two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to determine the most effective model for classifying public sentiment regarding the impact of AI in Indonesia. Research data were collected from the social media platform X/Twitter and processed through a series of stages, including text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), and two experimental scenarios: baseline testing and optimization via hyperparameter tuning. The model performance was evaluated based on accuracy, precision, recall, F1-score, and AUC metrics. The results indicate that SVM consistently outperformed RF, with the best model achieving an accuracy of 67.44% and an AUC of 0.73. The optimization process successfully improved the RF’s performance to 65.36% with an AUC of 0.71, but did not alter the SVM’s performance, whose configuration was already optimal. While SVM achieved higher accuracy, the difference was not found to be statistically significant. Therefore, this study suggests that SVM holds a slight performance advantage, but both models exhibit comparable robustness for this case study. This study contributes by providing a direct, in-depth comparison of these two popular models on Indonesian-language AI sentiment, which includes comprehensive diagnostic analyses to explain the performance differences.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - I Wayan Suasnawa
AU  - Ida Bagus Putra Manuaba
AU  - Ni Gusti Ayu Putu Harry Saptarini
AU  - I Gusti Ngurah Bagus Catur Bawa
AU  - Anak Agung Ngurah Gde Sapteka
AU  - I Nyoman Eddy Indrayana
PY  - 2025
DA  - 2025/10/31
TI  - Sentiment Analysis of Indonesian Tweets on AI Impact: A Comparison between Random Forest and SVM
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2025 (ICOSTAS-EAS 2025)
PB  - Atlantis Press
SP  - 516
EP  - 523
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-878-3_57
DO  - 10.2991/978-94-6463-878-3_57
ID  - Suasnawa2025
ER  -