Yuliasari, Putri (2024) Analisis Sentimen Perbandingan Algoritma Svm Dan Naive Bayes Pada Capres 2024 Di Indonesia. Other thesis, Universitas Islam Riau.

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Abstract

This research aims to analyze public opinion sentiment towards candidates president of 2024 in Indonesia using the comments used in YouTube platform. Two commonly used classification algorithms are SVM and Naïve Bayes. To evaluate its effectiveness in predicting sentiment. YouTube comments were collected and classified as positive negative and neutral. Using sentiment analysis techniques. Power gained then divided into two sets of training data to train the model and test data for testing model performance. Results from a comparison of the SVM and Naïve Bayes algorithms shows differences in accuracy, precision, recall and F1-score. This research can provide valuable insight into understanding opinions about candidates president and comparative effectiveness of classification algorithms in analysis sentiment. Keywords: Sentiment Analysis, Public Opinion, 2024 Presidential Candidates, YouTube, SVM, Naïve Bayes

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