Sentiment Analysis of Public Trust Towards Islamic Boarding School on Social Media Using Machine Learning Method - Eko Prasetio Widhi; Much.def putra sholihin; Aziz Musthafa; Nurhana Marantika (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
This study aims to analyse public sentiment towards Islamic boarding schools on social media using a machine learning approach. A total of 1,905 cleaned comments were collected from two platforms, Twitter (X) and YouTube, and then processed through the CRISP-DM stages, which include business understanding, data preparation, modelling, evaluation, and deployment. Pre-processing steps such as tokenisation, stemming, and labelling were applied to prepare the text data for analysis. The sentiment classification was carried out using five machine learning algorithms: Naïve Bayes, Decision Tree, Neural Network, Support Vector Machine (SVM), and Random Forest. The evaluation results revealed that Random Forest outperformed other models, achieving the highest accuracy (79%), F1-score (79%), precision (80%), and recall (79%), indicating a strong balance in identifying sentiment classes accurately and consistently. Additionally, the research implemented interactive visualisations using Streamlit, enabling the public and stakeholders to understand sentiment trends in a clear, data-driven format. These findings are expected to serve as a strategic foundation for Islamic boarding schools in building a positive image in the digital space and for further development of AI-based opinion monitoring systems.