Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia - Henderi, Henderi; Asro, Asro; Sulaiman, Agus; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; AlQudah, Mashal (Journal of Applied Data Sciences, 2025)
This study explores the potential of sentiment analysis in providing valuable insights into education in Indonesia based on comments from the YouTube platform. Utilizing the Naive Bayes Classifier method, this research analyzed 13,386 processed comments out of 17,920 original comments. The results show that 53.8% of comments were negative, while 28.5% were positive, and 17.7% were neutral, reflecting diverse perspectives on existing educational issues. The Accuracy of this model reached up to 72.51% with testing on various sample sizes (10%-30%), indicating the model's effectiveness in identifying sentiments. Although the model tends to classify comments as unfavorable, this opens opportunities for introspection and improvement within the educational system. Further analysis with a word cloud revealed dominant keywords, indicating areas that require more attention in public discussions about education. By leveraging this sentiment analysis, the study offers practical and valuable guidance for policymakers to reflect on and enhance educational strategies and policies in Indonesia. This research measures public reactions and aims to foster more constructive and inclusive discussions about the sustainable development of education in Indonesia.