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Dinta Sabrina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Analysis of Sentiment Towards Educational Services in Modern Islamic Boarding Schools using the Naïve Bayes Method Minardi, Joko; Noor Azizah; Ahmad Saefudin; Alzena Dona Sabilla; Dinta Sabrina; Yulia Savika Rahmi
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15861

Abstract

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.
Co-Authors Ahmad Saefudin Alzena Dona Sabilla Joko Minardi, Joko Noor Azizah Yulia Savika Rahmi