Analyzing Opinion Polarization on Joko Widodo's Diploma Using Machine Learning

Julianti Julianti; Farid Wajidi; Musawwir Musawwir

doi:10.24014/ijaidm.v9i1.38616

Analyzing Opinion Polarization on Joko Widodo's Diploma Using Machine Learning

Julianti Julianti, Farid Wajidi, Musawwir Musawwir

Abstract

The issue of the authenticity of former President Joko Widodo's diploma has become a hot topic of debate in the digital space, especially in the comments section of Kompas TV's YouTube channel. The wide diversity of opinions reflects a polarization of public opinion that is interesting to analyze further. Given the large amount of text data from public comments, manual analysis is ineffective, so a technology-based approach is needed to systematically group opinions. Therefore, this study was conducted to analyze the polarization of public opinion using a machine learning approach. Two classification algorithms, Naive Bayes and Random Forest, were used to distinguish between pro and con public comments on the issue. Data was obtained through an automated collection process (web scraping), followed by text pre-processing and word weighting using the TF-IDF (Term Frequency–Inverse Document Frequency) method. The test results showed that the Random Forest algorithm performed best with an accuracy of 91%, while Naïve Bayes only achieved 74%. This shows that the Random Forest method is more effective than the Naïve Bayes approach in detecting unstructured text patterns. This study concludes that machine learning can be used effectively to identify trends in public opinion on social media and can serve as a basis for further research using word embedding and deep learning models

Keywords

Machine Learning; Naïve Bayes; Public Opinion Polarization; Random Forest; Sentiment Analysis

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DOI: http://dx.doi.org/10.24014/ijaidm.v9i1.38616

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