Analysis of Sentiment Towards Educational Services in Modern Islamic Boarding Schools using the Naïve Bayes Method
DOI:
https://doi.org/10.15294/sji.v11i4.15861Keywords:
Analysis Sentiment, Educational Services, Islamic Boarding Schools , Naïve Bayes, CRISPDMAbstract
Purpose: This study aims to analyze public sentiment regarding educational services in modern Islamic boarding schools using the Naïve Bayes method. The findings provide recommendations for improving educational quality.
Methods: The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, utilizing web scraping techniques to collect data from social media and online discussion forums. The Naïve Bayes algorithm is used for sentiment classification.
Result: A dataset of 387 reviews was analyzed, showing that 82.8% of reviews were positive, while 17.2% were negative. The model achieved an accuracy of 88%.
Novelty: Unlike previous studies, this research focuses specifically on modern Islamic boarding schools, employing machine learning for sentiment classification to provide actionable recommendations.
