The Public Sentiment Analysis on the Implementation of the Independent Curriculum through YouTube Comment Data Using K-Nearest Neighbors and Naïve Bayes Classification Algorithms. - rinaldi rinaldi; Sutedi (LPPM Universitas Muhammadiyah Kotabumi, 2024)
This research aims to conduct a public sentiment analysis on the implementation of the Merdeka Curriculum through YouTube comment data using the K-Nearest Neighbors (K-NN) and Naïve Bayes classification algorithms. The Merdeka Curriculum is an educational program launched by the Indonesian government in 2020 to improve the quality of education in Indonesia. The research method used is quantitative, with stages including data collection, data preprocessing, and sentiment classification modeling using the K-NN and Naïve Bayes algorithms. The research results show that the K-NN algorithm performs better than the Naïve Bayes algorithm in classifying public sentiment towards the implementation of the Merdeka Curriculum. The K-NN algorithm with the best k value of 11 achieved an accuracy of 79.70%, precision of 77.20%, recall of 79.70%, and an f1-score of 77.65%, with an AUC value of 0.91, indicating excellent classification performance. In contrast, the Naïve Bayes algorithm achieved an accuracy of 53.82%, precision of 72%, recall of 53.82%, an f1-score of 59%, and an AUC value of 0.75, which falls into the fair category.