Data-Driven Insights of the Ecotheology Implementation at Islamic Schools in Indonesia using Machine Learning
DOI:
https://doi.org/10.35877/454RI.asci4572Keywords:
data science, ecotheology, environmental awareness, Islamic schools, machine learningAbstract
Ecotheology is an integration of religious values towards awareness of environmental preservation. Indonesia’s Ministry of Religious Affairs has identified ecotheology as a strategic program, including Islamic school students. Therefore, this study aims to reveal the understanding, implementation, challenges, and opportunities of ecotheology in Islamic schools. This research applies data science and machine learning algorithms to analyze a large student dataset, 22,933 data from 32 provinces, with a 41-question validated questionnaire (Cronbach’s Alpha = 0.765, Kappa = 0.791). This research uses K-Means and PCA for clustering to group students by ecotheology awareness and implementation, Association Rules with Apriori algorithm to identify knowledge sources, obstacles, and program linkages, classification using ensemble learning with CatBoost as the best model with 98.71% accuracy, and sentiment analysis using RoBERTa-based Indonesian model on open responses. This research found that students’ understanding of ecotheology is high, with most learning from teachers and others gaining knowledge from social media and books, while implementation remains moderate due to limited programs, policies, and subject integration. In accordance with student’s understanding, the sentiment analysis revealed neutral tones in suggestions but mostly positive expectations, with students desiring more practical, Quran-linked, and community-based activities.
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2026-04-30
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Maylawati, D. S., Slamet, C., Umana, M. K., Rifa’i, A. R., Mulyana, R., Ramdhani, M. A., & Syafi’i, S. (2026). Data-Driven Insights of the Ecotheology Implementation at Islamic Schools in Indonesia using Machine Learning. Journal of Applied Science, Engineering, Technology, and Education, 8(1), 175–195. https://doi.org/10.35877/454RI.asci4572
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Copyright (c) 2026 Dian Sa'adillah Maylawati, Cepy Slamet, Muhammad Khalifa Umana, Akhmad Ridlo Rifa'i, Rohmat Mulyana, Muhammad Ali Ramdhani, Syafi’i Syafi’i (Author)
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