Data-Driven Insights of the Ecotheology Implementation at Islamic Schools in Indonesia using Machine Learning

Dian Sa'adillah Maylawati, Cepy Slamet, Muhammad Khalifa Umana, Akhmad Ridlo Rifa'i, Rohmat Mulyana, Muhammad Ali Ramdhani, Syafi’i Syafi’i
https://doi.org/10.35877/454RI.asci4572

Abstract

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.

Keywords

Downloads

Download data is not yet available.

References

  1. Ach. Syaiful Islam, Suhermanto Ja’far, & Ahmad Sunawari Long. (2024). Islam and Eco-Theology: Perspectives and Strategies of Muhammadiyah in Addressing the Environmental Crisis. Fikri?: Jurnal Kajian Agama, Sosial Dan Budaya, 9(2), 170–181. https://doi.org/10.25217/jf.v9i2.4821
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, a I. (1996). Fast discovery of association rules. In Advances in knowledge discovery and data mining (Vol. 12, pp. 307–328). http://www.cs.helsinki.fi/hannu.toivonen/pubs/advances.pdf
    Alzamzami, F., Hoda, M., & Saddik, A. El. (2020). Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation. IEEE Access, 8, 101840–101858. https://doi.org/10.1109/ACCESS.2020.2997330
    Anabaraonye, B., Onnoghen, U. N., Orji, I. E., Ewa, B. O., & Olisah, N. C. (2024). Enhancing Eco-Theology for Climate Change Education and Sustainable Development in Nigeria. TELEIOS: Jurnal Teologi Dan Pendidikan Agama Kristen, 4(2), 172–184. https://doi.org/10.53674/teleios.v4i2.146
    Andira, M. A., Pallu, D., Sari, I., & Maria, H. (2024). Merajut Spiritualitas Dan Lingkungan: Tinjauan Teologis Terhadap Keselamatan Alam. Jurnal Silih Asih, 1(2), 10–18. https://doi.org/10.54765/silihasih.v1i2.53
    Aranganayagi, S., & Thangavel, K. (2008). Clustering categorical data using silhouette coefficient as a relocating measure. Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007. https://doi.org/10.1109/ICCIMA.2007.127
    Aruan, A. K. (2024). Postcolonial Typology: A Pedagogical Note on the Field of Ecotheology. Religions, 15(12), 1422. https://doi.org/10.3390/rel15121422
    Asare, J. (2022). Eco-Theology: Content Analysis Of The Teaching Contents Employed For Environmental Education In The Religious Education Curricula From Basic Schools To High Schools In Ghana. https://doi.org/10.14293/S2199-1006.1.SOR-.PP5DV64.v1
    Asmanto, E., Miftakhurrohmat, A., & Asmarawati, D. (2016). Dialektika Spiritualitas Ekologi (Eco-Spirituality) Perspektif Ekoteologi Islam pada Petani Tambak Udang Tradisional Kabupaten Sidoarjo. Universitas Islam Negeri Sulthan Thaha Saifuddin Jambi.
    B R, A., Pulari, S. R., Murugesh, T. S., & Vasudevan, S. K. (2024). Machine Learning. CRC Press. https://doi.org/10.1201/9781032676685
    Ben Salem, K., & Ben Abdelaziz, A. (2021). Principal component analysis (PCA). Tunisie Medicale, 99(4), 383–389. https://doi.org/10.1515/9783110629453-078
    Bentéjac, C., Csörg?, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937–1967. https://doi.org/10.1007/s10462-020-09896-5
    Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. BMJ, 314(7080), 572–572. https://doi.org/10.1136/bmj.314.7080.572
    Cameron, K. E., & Bizo, L. A. (2019). Use of the game-based learning platform KAHOOT! to facilitate learner engagement in animal science students. Research in Learning Technology, 27. https://doi.org/10.25304/rlt.v27.2225
    Candido, C., Blanco, A. C., Medina, J., Gubatanga, E., Santos, A., Ana, R. S., & Reyes, R. B. (2021). Improving the consistency of multi-temporal land cover mapping of Laguna lake watershed using light gradient boosting machine (LightGBM) approach, change detection analysis, and Markov chain. Remote Sensing Applications: Society and Environment, 23. https://doi.org/10.1016/j.rsase.2021.100565
    Charim, A., Basuki, S., & Akbi, D. R. (2019). Detect Malware in Portable Document Format Files (PDF) Using Support Vector Machine and Random Decision Forest. Jurnal Online Informatika, 3(2), 99–102.
    Chen, S. (2025). Sentiment Analysis Techniques for Deep Learning Classification and Comparison. Theoretical and Natural Science, 86(1), 74–80. https://doi.org/10.54254/2753-8818/2025.20342
    Chen, Z. (2025). Wine Quality Prediction with Ensemble Trees: A Unified, Leak-Free Comparative Study.
    Cholil, S., & Parker, L. (2021). Environmental education and eco-theology: insights from Franciscan schools in Indonesia. Environmental Education Research, 27(12), 1759–1782. https://doi.org/10.1080/13504622.2021.1968349
    Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., & Salakhutdinov, R. (2019). Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.
    Dalihade, R. D. (2021). Eko-Spiritualitas Trinitaris: Sebuah Upaya Membangun Spiritualitas Lingkungan terhadap Krisis Lingkungan (Reklamasi Pantai) di Manado. Aradha: Journal of Divinity, Peace and Conflict Studies, 1(2), 199–216. https://doi.org/10.21460/aradha.2021.12.625
    Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
    Direktorat Jendral Pendidikan Islam Kementerian Agama RI. (2025). EMIS: Gerbang Data Pendidikan Kementerian Agama. EMIS 4.0. https://emis.kemenag.go.id/
    Doede, N., Merkel, P., Kriwall, M., Stonis, M., & Behrens, B.-A. (2024). Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard. Production Engineering. https://doi.org/10.1007/s11740-024-01298-8
    Edelsbrunner, P. A., Simonsmeier, B. A., & Schneider, M. (2025). The Cronbach’s Alpha of Domain-Specific Knowledge Tests Before and After Learning: A Meta-Analysis of Published Studies. Educational Psychology Review, 37(1), 4. https://doi.org/10.1007/s10648-024-09982-y
    Elkabalawy, M., Al-Sakkaf, A., Mohammed Abdelkader, E., & Alfalah, G. (2024). CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors. Sustainability, 16(17), 7249. https://doi.org/10.3390/su16177249
    Fauzi, R. S., Arianisa, S., & Vahira, V. A. (2025). Finding Important Patterns of Sadaqah and Waqf Transactions in the Eid Charity Global Donation using Frequent Pattern Growth. Khazanah Journal of Religion and Technology, 3(1), 1–8. https://doi.org/https://doi.org/10.15575/kjrt.v3i1.1641
    Florek, P., & Zagda?ski, A. (2023). Benchmarking state-of-the-art gradient boosting algorithms for classification.
    Forero, C. G. (2023). Cronbach’s Alpha. In Encyclopedia of Quality of Life and Well-Being Research (pp. 1505–1507). Springer International Publishing. https://doi.org/10.1007/978-3-031-17299-1_622
    Ghatora, P. S., Hosseini, S. E., Pervez, S., Iqbal, M. J., & Shaukat, N. (2024). Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM. Big Data and Cognitive Computing, 8(12), 199. https://doi.org/10.3390/bdcc8120199
    Gill, M. S., Westermann, T., Steindl, G., Gehlhoff, F., & Fay, A. (2024). Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance.
    Hamka, M., & Ramdhoni, N. (2022). K-Means cluster optimization for potentiality student grouping using elbow method. 060011. https://doi.org/10.1063/5.0108926
    Hancock, J. T., & Khoshgoftaar, T. M. (2020a). CatBoost for big data: an interdisciplinary review. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00369-8
    Hancock, J. T., & Khoshgoftaar, T. M. (2020b). CatBoost for big data: an interdisciplinary review. Journal of Big Data, 7(1), 94. https://doi.org/10.1186/s40537-020-00369-8
    Hartigan, A., & Wong, M. A. (1979). A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. https://doi.org/10.2307/2346830
    Hartigan, J. A., & Wong, M. A. (2006). Algorithm AS 136: A K-Means Clustering Algorithm. Applied Statistics. https://doi.org/10.2307/2346830
    Hove, R. (2024). Eco-theology: Reimagining the image of God in the context of creation and care in Africa. Wawasan: Jurnal Ilmiah Agama Dan Sosial Budaya, 9(1), 37–46. https://doi.org/10.15575/jw.v9i1.34356
    Ibrahim, A. A., Ridwan, R. L., Muhammed, M. M., Abdulaziz, R. O., & Saheed, G. A. (2020). Comparison of the CatBoost Classifier with other Machine Learning Methods. International Journal of Advanced Computer Science and Applications, 11(11), 738–748. https://doi.org/10.14569/IJACSA.2020.0111190
    Januardi. (2025, January 24). Madrasah Jadi Pilar Pendidikan Keagamaan Berwawasan Lingkungan. Radio Republik Indonesia. https://www.rri.co.id/papua-barat/iptek/1277718/madrasah-jadi-pilar-pendidikan-keagamaan-berwawasan-lingkungan
    Kgatle, M. S., & Chigorimbo, J. (2024). Towards Holistic Healing: A Pentecostal Ecotheological Perspective. Religions, 15(12), 1479. https://doi.org/10.3390/rel15121479
    Laksono, G. E. (2022). Mewujudkan Kesadaran Lingkungan melalui Pendidikan Agama Islam berbasis Ecotheology Islam. Jurnal Kependidikan, 10(2), 247–258. https://doi.org/10.24090/jk.v10i2.8043
    Lan, H., & Pan, Y. (2019a). A crowdsourcing quality prediction model based on random forests. Proceedings - 18th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2019, 315–319. https://doi.org/10.1109/ICIS46139.2019.8940306
    Lan, H., & Pan, Y. (2019b). A crowdsourcing quality prediction model based on random forests. Proceedings - 18th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2019, 315–319. https://doi.org/10.1109/ICIS46139.2019.8940306
    Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.
    Li, M., Gao, Q., & Yu, T. (2023). Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters. BMC Cancer, 23(1), 799. https://doi.org/10.1186/s12885-023-11325-z
    Li, S., Da Xu, L., & Zhao, S. (2015). The internet of things: a survey. Information Systems Frontiers, 17(2), 243–259.
    Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach.
    Mardhiah, I., Aulia, R. N., & Narulita, S. (2014). Konsep Gerakan Ekoteologi Islam Studi atas Ormas NU dan Muhammadiyah. Jurnal Studi Al-Qur’an Membangun Tradisi Berfikir Qur’ani, 10(1), 1–14.
    Mateo, J., Rius-Peris, J. M., Maraña-Pérez, A. I., Valiente-Armero, A., & Torres, A. M. (2021). Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis. Biocybernetics and Biomedical Engineering, 41(2), 792–801. https://doi.org/10.1016/j.bbe.2021.04.015
    Munde, A. (2024). The Machine Learning Pipeline. In Deep Learning Concepts in Operations Research (pp. 226–243). Auerbach Publications. https://doi.org/10.1201/9781003433309-18
    Ngalawa, C. M., & Mponela, U. (2024). Social Sentiment Analysis. International Journal of Advanced Research in Science, Communication and Technology, 669–674. https://doi.org/10.48175/IJARSCT-22291
    Pangihutan, P., & Jura, D. (2023). Ecotheology and Analysis of Christian Education in Overcoming Ecological Problems. International Journal of Science and Society, 5(1), 13–27. https://doi.org/10.54783/ijsoc.v5i1.621
    Permadi, V. A., Tahalea, S. P., & Agusdin, R. P. (2023). K-Means and Elbow Method for Cluster Analysis of Elementary School Data. PROGRES PENDIDIKAN, 4(1), 50–57. https://doi.org/10.29303/prospek.v4i1.328
    Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 2018-Decem, 6638–6648.
    Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2019). CatBoost: unbiased boosting with categorical features.
    Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1), 5485–5551.
    Rezaei, N., & Jabbari, P. (2022). Introduction to machine learning. In Immunoinformatics of Cancers (pp. 53–69). Elsevier. https://doi.org/10.1016/B978-0-12-822400-7.00012-9
    Rohman, A., Kurniawan, E., Syifauddin, M., Muhtamiroh, S., & Muthohar, A. (2024). Religious Education For The Environment: Integrating Eco-Theology In The Curriculum of Islamic Religious And Character Education To Enhance Environmental Education In Indonesia. Nadwa: Jurnal Pendidikan Islam, 18(2), 201–226. https://doi.org/10.21580/nw.2024.18.2.21094
    Romdloni, M. A., Fitriyah, F. K., Djazilan, M. S., & Taufiq, M. (2024). Eco-Theology; Habits and Lifestyle of Santri in Indonesian Islamic Boarding Schools. E3S Web of Conferences, 482, 04030. https://doi.org/10.1051/e3sconf/202448204030
    Romdloni, M. A., & Sukron Djazilan, M. (2019). Kiai dan Lingkungan Hidup; Revitalisasi Krisis Ekologis Berbasis Nilai Keagamaan di Indonesia. Journal of Islamic Civilization, 1(2), 119–129. https://doi.org/10.33086/jic.v1i2.1322
    Runtuwene, H. C. M. (2025). Ecotheology: Integrating Faith, Creation Care, and Contextual Practice in Indonesian Protestant Congregations. Educatio Christi, 6(1), 145–170. https://doi.org/10.70796/educatio-christi.v6i1.215
    Sabtina, D., & Mahariah, M. (2025). Internalizing Islamic Ecotheology through School Culture to Foster Eco-Character. Halaqa: Islamic Education Journal, 9(2), 21–41. https://doi.org/https://doi.org/10.21070/halaqa.v9i2.1754
    Saini, A. (2021). AdaBoost Algorithm – A Complete Guide for Beginners. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/
    Sammouda, R., & El-Zaart, A. (2021). An Optimized Approach for Prostate Image Segmentation Using K?Means Clustering Algorithm with Elbow Method. Computational Intelligence and Neuroscience, 2021(1). https://doi.org/10.1155/2021/4553832
    Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.
    Saputra, D. B., Atina, V., & Nastiti, F. E. (2024). Model CRISP-DM Pada Prediksi Nasabah Kredit Menggunakan Algoritma Random Forest. IDEALIS?: InDonEsiA JournaL Information System, 7(2), 240–247. https://doi.org/10.36080/idealis.v7i2.3244
    Saputro, D. E., & Gunadi, D. (2021). Ekoteologi Komunitas Sedulur Sikep. Jurnal Abdiel: Khazanah Pemikiran Teologi, Pendidikan Agama Kristen Dan Musik Gereja, 5(1), 42–56. https://doi.org/10.37368/ja.v5i1.243
    Sayeed, M. A. (2020). Detecting Crows on Sowed Crop Fields using Simplistic Image processing Techniques by Open CV in comparison with TensorFlow Image Detection API. International Journal for Research in Applied Science and Engineering Technology, 8(3), 61–73. https://doi.org/10.22214/ijraset.2020.3014
    Shahapur, V., Shetty, A., Shetty, C. P., Gayathri, & Shetty, P. (2022). Data Science. International Journal of Advanced Research in Science, Communication and Technology, 487–495. https://doi.org/10.48175/IJARSCT-2904
    Shlens, J. (2014). A tutorial on principal component analysis. ArXiv Preprint ArXiv:1404.1100.
    Shook, E. (2023). Data Science. In International Encyclopedia of Geography (pp. 1–5). Wiley. https://doi.org/10.1002/9781118786352.wbieg2050
    Slamet, C., Rahman, A., Ramdhani, M. A., & Dharmalaksana, W. (2016). Clustering the verses of the holy qur’an using K-means algorithm. Asian Journal of Information Technology. https://doi.org/10.3923/ajit.2016.5159.5162
    Solichin, M. M. (2017). Pendidikan Agama Islam Bewawasan Spiritualitas EkologI: Telaah Materi dan Model Pembelajaran. Al-Tahrir, 17(2), 471–494.
    Syafaruddin, B. (2025). Ecotheology in the Perspective of Islamic Education: A Conceptual Review. ETDC: Indonesian Journal of Research and Educational Review, 4(3), 720–731. https://doi.org/10.51574/ijrer.v4i3.3253
    Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, H. S., Ghafouri, A., Menegali, M., Huang, Y., Krikun, M., Lepikhin, D., Qin, J., … Le, Q. (2022). LaMDA: Language Models for Dialog Applications.
    Ting, K. M. (2017). Confusion Matrix. In Encyclopedia of Machine Learning and Data Mining. https://doi.org/10.1007/978-1-4899-7687-1_50
    United Nations. (2015, January). Sustainable Development Goals (SDGs) 2030. Sdgs.Un.Org. https://sdgs.un.org
    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. ArXiv Preprint ArXiv:1706.03762.
    Widianto, A. A., Putra, A. K., Alam, M., Fatanti, M. N., Thoriquttyas, T., Yuanda, B., Afiah, A. N., & Sulistywati, E. (2023). Practising Eco-Theology: Pesantren and Green Education in Narmada Lombok, Nusa Tenggara Barat (NTB), Indonesia (pp. 118–125). https://doi.org/10.2991/978-2-38476-078-7_14
    Widiarto, W., & Wilaela, W. (2022). Ekoteologis Perspektif Agama-Agama. TOLERANSI: Media Ilmiah Komunikasi Umat Beragama, 13(2), 103–124.
    Worotikan, D. B., Baleona, Y., Katanggung, A. E., Dehoop, B. S., Pietersz, K. V, Samudra, A. M., Pangkerego, T. O., Pieter, L., Momongan, E. T., & Kapugu, J. (2024). Menyatukan spiritualitas dan ekologi: Peran vital penyuluhan agama dalam pelestarian lingkungan. Merenda: Jurnal Penyuluh Agama, 1(1), 5–9.
    Yan, Y. (2022). Machine Learning Fundamentals. In Machine Learning in Chemical Safety and Health (pp. 19–46). Wiley. https://doi.org/10.1002/9781119817512.ch2
    Yenduri, G., M, R., G, C. S., Y, S., Srivastava, G., Maddikunta, P. K. R., G, D. R., Jhaveri, R. H., B, P., Wang, W., Vasilakos, A. V., & Gadekallu, T. R. (2023). Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions.
    Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469–477. https://doi.org/10.1016/j.gsf.2020.03.007
    Zhang, Y., Zhao, Z., & Zheng, J. (2020). CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. Journal of Hydrology, 588. https://doi.org/10.1016/j.jhydrol.2020.125087

How to Cite

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

Copyright & license

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)