Peningkatan Performa Naive Bayes dengan Fitur Chi-Square pada Analisis Sentimen Komentar Pengguna Aplikasi Netflix
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.532Keywords:
Sentiment Analysis; Naive Bayes; Chi-Square; Feature Extraction; Performance ImprovementAbstract
This study discusses sentiment analysis using the Naïve Bayes algorithm with Chi-Square. The purpose of this study is to determine the effect of Chi-Square feature selection on the performance of the Naïve Bayes algorithm in analyzing document sentiment. The research data was taken from Netflix Application user comments. Testing was carried out by analyzing document sentiment with and without Chi-Square feature selection. Furthermore, it was evaluated using the accuracy, precision, and recall methods. The results of this study are that the addition of CS features to NB significantly improves all evaluation metrics, especially recall and F1-score, indicating that additional features help improve the model's ability to understand data. The combination of NB + CS with a 70:30 split gives the best results, making it the optimal choice.
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