Islamic Green Economy in Virtual Events and Implications for Energy Policy: A Text Analytics
Abstract
Purpose: This study explores the contemporary discourse on the Islamic green economy from virtual events, e.g., YouTube discussions and recorded Zoom webinars, using a text-mining approach. It aims to identify the dominant themes and conceptual relationships characterizing current discussions on the Islamic green economy. Methodology: This study applies machine-learning text analytics to transcripts of discussions among experts on the Islamic green economy from selected virtual events. We used a Python-based tool, i.e., Orange Data Mining, to address four research questions. The analysis includes word analytics using word clouds, hierarchical clustering, topic modeling via Latent Dirichlet Allocation (LDA), and word network analysis. Results: The findings show that the Islamic green economy discourse is consistently associated with key themes, e.g., maqasid al-Shariah, public policy, sustainable development, and environmental responsibility. Topic modeling reveals close relationships among green economy, Islamic development, sustainability, and government, indicating that the discourse incorporates tauhidic values and maqasid al-Shariah. Network analysis further demonstrates that the Islamic green economy extends beyond environmental concerns by emphasizing social justice and ecological balance. Conclusion: The Islamic green economy provides an integrated framework connecting Islamic values, sustainable development, environmental responsibility, and social justice. Implications: The study is limited by its reliance on transcripts from selected virtual events, which may not fully represent the broader discourse on the Islamic green economy. Therefore, the findings should be interpreted within the scope of the selected discussions. Originality: This study contributes an empirical text mining perspective by systematically examining the conceptual structure of Islamic green economy discourse in contemporary virtual events.
References
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Bao, C., & Wang, Y. (2021). A Survey of Word Cloud Visualization. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics. 33(4). 532–544. https://doi.org/10.3724/SP.J.1089.2021.18811
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Fattah, R. (2016). Twitter text mining untuk informasi gempa bumi menggunakan TF-IDF di Indonesia. Universitas Islam Negeri Maulana Malik Ibrahim.
Gruzd, A., Paulin, D., & Haythornthwaite, C. (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics. 3(3). 46–71. https://doi.org/10.18608/jla.2016.33.4
Haleem, A., Javaid, M., Asim, M., Pratap, R., & Suman, R. (2022). International Journal of Intelligent Networks Artificial intelligence ( AI ) applications for marketing : A literature-based study. International Journal of Intelligent Networks. 3. 119–132. https://doi.org/10.1016/j.ijin.2022.08.005
Hermawan, A., & Jowensen, I. (2023). Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine. JST (Jurnal Sains & Tekhnologi). 12(1). 129–137. https://doi.org/10.23887/jstundiksha.v12i1.52358
Hidayati, Y. E., & Addainuri, M. I. (2024). Green Economy and Islamic Economy : Towards Accelerating SDGs. Jurnal Manajemen Bisnis. 11(2). 1413–1430. https://doi.org/10.33096/jmb.v11i2.908
Hou-Liu, J. (2018). Benchmarking and Improving Recovery of Number of Topics in Latent Dirichlet Allocation Models. https://vixra.org/abs/1801.0045
Huang, P., & Butts, C. T. (2024). Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study. Social Networks. 76. 51–67. https://doi.org/10.1016/j.socnet.2023.07.001
Hudaefi, F. A., Caraka, R. E., & Wahid, H. (2021). Zakat administration in times of COVID-19 pandemic in Indonesia: a knowledge discovery via text mining. International Journal of Islamic and Middle Eastern Finance and Management. 15(2). 271–286. https://doi.org/10.1108/IMEFM-05-2020-0250
Hudaefi, F. A., Hassan, M. K., Abduh, M., & Beik, I. S. (2023). Knowledge discovery of zakat administration worldwide from YouTube and Zoom via text mining. Journal of Islamic Accounting and Business Research. 14(1). 159–180. https://doi.org/10.1108/JIABR-03-2022-0067
Kobayashi, V., el.al. (2017). Text Mining in Organizational Research. Organizational Research Methods 21(3). 733-765. https://doi.org/10.1177/1094428117722619
Kurniawan, H., Gunawan, R., & Rahayu, S. (2025). Green Economy dalam Perspektif Ekonomi Syariah : Solusi untuk Pembangunan Berkelanjutan. Prosiding Seminar Nasional Teknologi Komputer Dan Sains. 3(1). 361–368.
Kusnadi, K., Gata, W., & Nova Arviantino, F. (2022). Aplikasi Algoritma Kruskal dan Sollin Pada Jaringan Transmisi Nasional Provinsi Sulawesi Selatan. Metik Jurnal. 6(1). 8–17. https://doi.org/10.47002/metik.v6i1.260
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Lauritzen, S., Rinaldo, A., & Sadeghi, K. (2018). Random Networks, Graphical Models and Exchangeability. Journal of the Royal Statistical Society Series B: Statistical Methodology. 80(3). 481–508. https://doi.org/10.1111/rssb.12266
Le, T. M. V., & Lauw, H. W. (2016). Word clouds with latent variable analysis for visual comparison of documents. IJCAI International Joint Conference on Artificial Intelligence.
Munthe, H. S., & Handoko, R. (2023). Mengungkap Pola dan Hubungan dalam Data Teks. Sumatera : CV. Gita Lentera.
Nyimbili, F., & Nyimbili, L. (2024). Types of purposive sampling techniques with their examples and application in qualitative research studies. British Journal of Multidisciplinary and Advanced Studies. 5(1). 90-99. https://doi.org/10.37745/bjmas.2022.0419
Rahmah Kusuma, N., Hamidah, I., & Fitriani, N. (2022). Pengelolaan Sumber Daya Alam Berbasis Ekonomi Hijau dalam Perspektif Syariah Untuk Mendukung Pembangunan Berkelanjutan di Indonesia. Prosiding Konasi. 4(5). 142–153.
Ramya, K., & Suganya, K. (2018). A Significant Approach for Graph Theory and Its Applications. International Journal of Scientific Research in Science and Technology. 4(11). 247–250. https://doi.org/10.32628/ijsrst18401146
Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review. 56(8). 8219–8264. https://doi.org/10.1007/s10462-022-10366-3
Rinaldi, A. M., & Russo, C. (2021). Using a multimedia semantic graph for web document visualization and summarization. Multimedia Tools and Applications. 80(3). 3885–3925.
Safna, F. F., & Apriantoro, M. S. (2024). Exploring the Effectiveness of BWI’s Digital Waqf Campaign: A Netnographic Analysis of Social Media Engagement. Ethica: International Journal of Humanities and Social Science Studies. 2(1). 1-20.
Schwarz, C. (2018). Ldagibbs: A command for topic modeling in stata using latent Dirichlet allocation. Stata Journal. 18(1). 101–117. https://doi.org/10.1177/1536867x1801800107
Takes, F. W., & Heemskerk, E. M. (2016). Centrality in the global network of corporate control. Social Network Analysis and Mining. 6(1). 1–18. https://doi.org/10.1007/s13278-016-0402-5
Tiwari, D., Nagpal, B., Bhati, B. S., Mishra, A., & Kumar, M. (2023). A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques. Artificial Intelligence Review Springer Netherlands. 56(11). https://doi.org/10.1007/s10462-023-10472-w
Ulfah, I. F., Sukmana, R., Laila, N., & Sulaeman, S. (2024). A structured literature review on green sukuk (Islamic bonds): implications for government policy and future studies. Journal of Islamic Accounting and Business Research. 15(7). 1118–1133. https://doi.org/10.1108/JIABR-10-2022-0255
Vasiliauskaite, V., & Rosas, F. E. (2020). Understanding complexity via network theory: a gentle introduction. 1–19. http://arxiv.org/abs/2004.14845
Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems. 94. 101582. https://doi.org/10.1016/j.is.2020.101582
Widianto, A., Pebriyanto, E., Fitriyanti, F., & Marna, M. (2024). Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity. Journal of Dinda : Data Science, Information Technology, and Data Analytics. 4. 149–153. https://doi.org/10.20895/dinda.v4i2.1589
Wiratama, B. F., & Safitri, Z. (2023). Is Islamic Economy as that Green?. Journal Al-Muzara’Ah. 11(1). 103–118. https://doi.org/10.29244/jam.11.1.103-118
Yang, Y., Li, J., Shen, D., Nan, M., & Cui, Q. (2018). Evolutionary dynamics analysis of complex network with fusion nodes and overlap edges. Journal of Systems Engineering and Electronics. 29(3). 549-559. https://doi.org/10.21629/JSEE.2018.03.12
Zhao, F., Ren, X., Yang, S., Han, Q., Zhao, P., & Yang, X. (2021). Latent Dirichlet Allocation Model Training with Differential Privacy. IEEE Transactions on Information Forensics and Security. 16. 1290–1305. https://doi.org/10.1109/TIFS.2020.3032021
Zumbraegel, T. (2022). Inside the green state: Institutional organization, agency and governance. In Political power and environmental sustainability in gulf monarchies. Springer Nature. https://link.springer.com/chapter/10.1007/978-981-19-4431-4_1
Allam, I. (2026). Cosine Similarity For Interval Data: A Comprehensive Analysis Of Angular Distance Measures. https://doi.org/10.13140/RG.2.2.13804.12160
Ang, Y., Shafri, H., & Al-Habshi, M. (2025). Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with orange data mining. Agriculture and Natural Resources. 59(2). 1-11. https://doi.org/10.34044/j.anres.2025.59.2.01
Apriantoro, M. S., El Ashfahany, A., Tanasya, N., & Puspa, F. N. (2025). How Has Islamic Banking Windows Research Evolved? A Bibliometric Analysis of Trends, Influencers, and Sustainability Linkages. Suhuf. 37(1). 80-100. https://doi.org/10.23917/suhuf.v37i1.9935
Apriantoro, M. S., Herviana, J., Yayuli, Y., & Suratno, S. (2023). Sharia Financial Literacy: Research Trends and Directions for Future Inquiry. Journal of Islamic Economic Laws, 6(2). 19-40. https://doi.org/10.23917/jisel.v6i2.22396
Apriantoro, M. S., & Rahayuningsih, I. N. (2022). Implementation of Green Economy Through Integrated Urban Farming as Family Economic Resilience During The Pandemic: Maqasid Sharia Perspective. iqtishoduna Jurnal Ekonomi Islam. 11(1). 1–16. https://doi.org/10.54471/iqtishoduna.v11i1.1593
Apriantoro, M. S., Saifullah, M. F., & Hudaefi, F. A. (2025). How Does the Blue Economy Align with Sustainability? A Bibliometric Analysis of Trends and Themes. International Journal of Energy Economics and Policy. 15(5). 587–598. https://doi.org/10.32479/ijeep.20223
Apriantoro, M. S., Widyastuti, H., El Ashfahany, A., & Murtadla, A. A. (2024). Sustainability and Green Economy in Developmental Paradigms: A Bibliometric Analysis of Scholarly Trends and Transformations. Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE). 7(2). 4587–4610. https://doi.org/10.31538/iijse.v7i2.4786
Auliya, F. N., & Nurhadi, N. (2023). Towards A Sustainable Green Economy: Challenges And Opportunities For Long-Term Environmental And Economic Stability. Pengabmas Nusantara. 5(2). 97–102. https://doi.org/10.57214/pengabmas.v5i2.304
Babichev, S., Durnyak, B., Pikh, I., & Senkivskyy, V. (2020). An Evaluation of the Objective Clustering Inductive Technology Effectiveness Implemented Using Density-Based and Agglomerative Hierarchical Clustering Algorithms BT - Lecture Notes in Computational Intelligence and Decision Making (V. Lytvynenko, S. Babichev, W. Wójcik, O. Vynokurova, S. Vyshemyrskaya, & S. Radetskaya (eds.); pp. 532–553). Springer International Publishing.
Bao, C., & Wang, Y. (2021). A Survey of Word Cloud Visualization. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics. 33(4). 532–544. https://doi.org/10.3724/SP.J.1089.2021.18811
Campbell, Steve, Greenwood, Melanie, Prior, Sarah, Shearer, Toniele, Walkem, Kerrie, Young, Sarah, Bywaters, Danielle, & Walker, Kim. (2020). Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing. 25(8). 652–661. https://doi.org/10.1177/1744987120927206
Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D. L. R., Thompson, E. B., & Ashraf, I. (2023). A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry. 15(9). 1679. https://doi.org/10.3390/sym15091679
Dittman, Z., Munia, T. T. K., & Aviyente, S. (2021). Graph Theoretic Analysis of Multilayer EEG Connectivity Networks . Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/EMBC46164.2021.9629514
Dobesova, Z. (2024). Evaluation of Orange data mining software and examples for lecturing machine learning tasks in geoinformatics. Computer Applications in Engineering Education. 32(4). 1–18. https://doi.org/10.1002/cae.22735
Farahani, F. V, Karwowski, W., & Lighthall, N. R. (2019). Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review. Frontiers in Neuroscience. 13. 585. https://doi.org/10.3389/fnins.2019.00585
Fattah, R. (2016). Twitter text mining untuk informasi gempa bumi menggunakan TF-IDF di Indonesia. Universitas Islam Negeri Maulana Malik Ibrahim.
Gruzd, A., Paulin, D., & Haythornthwaite, C. (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics. 3(3). 46–71. https://doi.org/10.18608/jla.2016.33.4
Haleem, A., Javaid, M., Asim, M., Pratap, R., & Suman, R. (2022). International Journal of Intelligent Networks Artificial intelligence ( AI ) applications for marketing : A literature-based study. International Journal of Intelligent Networks. 3. 119–132. https://doi.org/10.1016/j.ijin.2022.08.005
Hermawan, A., & Jowensen, I. (2023). Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine. JST (Jurnal Sains & Tekhnologi). 12(1). 129–137. https://doi.org/10.23887/jstundiksha.v12i1.52358
Hidayati, Y. E., & Addainuri, M. I. (2024). Green Economy and Islamic Economy : Towards Accelerating SDGs. Jurnal Manajemen Bisnis. 11(2). 1413–1430. https://doi.org/10.33096/jmb.v11i2.908
Hou-Liu, J. (2018). Benchmarking and Improving Recovery of Number of Topics in Latent Dirichlet Allocation Models. https://vixra.org/abs/1801.0045
Huang, P., & Butts, C. T. (2024). Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study. Social Networks. 76. 51–67. https://doi.org/10.1016/j.socnet.2023.07.001
Hudaefi, F. A., Caraka, R. E., & Wahid, H. (2021). Zakat administration in times of COVID-19 pandemic in Indonesia: a knowledge discovery via text mining. International Journal of Islamic and Middle Eastern Finance and Management. 15(2). 271–286. https://doi.org/10.1108/IMEFM-05-2020-0250
Hudaefi, F. A., Hassan, M. K., Abduh, M., & Beik, I. S. (2023). Knowledge discovery of zakat administration worldwide from YouTube and Zoom via text mining. Journal of Islamic Accounting and Business Research. 14(1). 159–180. https://doi.org/10.1108/JIABR-03-2022-0067
Kobayashi, V., el.al. (2017). Text Mining in Organizational Research. Organizational Research Methods 21(3). 733-765. https://doi.org/10.1177/1094428117722619
Kurniawan, H., Gunawan, R., & Rahayu, S. (2025). Green Economy dalam Perspektif Ekonomi Syariah : Solusi untuk Pembangunan Berkelanjutan. Prosiding Seminar Nasional Teknologi Komputer Dan Sains. 3(1). 361–368.
Kusnadi, K., Gata, W., & Nova Arviantino, F. (2022). Aplikasi Algoritma Kruskal dan Sollin Pada Jaringan Transmisi Nasional Provinsi Sulawesi Selatan. Metik Jurnal. 6(1). 8–17. https://doi.org/10.47002/metik.v6i1.260
Latifah, E., & Abdullah, R. (2024). Prespektif Maqashid Syariah : Peran Ekonomi Hijau Dan Biru Dalam Mewujudkan Sustainable Development Goals. JISEF : Journal Of International Sharia Economics And Financial. 2(1). 1–22. https://doi.org/10.62668/jisef.v2i01.1128
Lauritzen, S., Rinaldo, A., & Sadeghi, K. (2018). Random Networks, Graphical Models and Exchangeability. Journal of the Royal Statistical Society Series B: Statistical Methodology. 80(3). 481–508. https://doi.org/10.1111/rssb.12266
Le, T. M. V., & Lauw, H. W. (2016). Word clouds with latent variable analysis for visual comparison of documents. IJCAI International Joint Conference on Artificial Intelligence.
Munthe, H. S., & Handoko, R. (2023). Mengungkap Pola dan Hubungan dalam Data Teks. Sumatera : CV. Gita Lentera.
Nyimbili, F., & Nyimbili, L. (2024). Types of purposive sampling techniques with their examples and application in qualitative research studies. British Journal of Multidisciplinary and Advanced Studies. 5(1). 90-99. https://doi.org/10.37745/bjmas.2022.0419
Rahmah Kusuma, N., Hamidah, I., & Fitriani, N. (2022). Pengelolaan Sumber Daya Alam Berbasis Ekonomi Hijau dalam Perspektif Syariah Untuk Mendukung Pembangunan Berkelanjutan di Indonesia. Prosiding Konasi. 4(5). 142–153.
Ramya, K., & Suganya, K. (2018). A Significant Approach for Graph Theory and Its Applications. International Journal of Scientific Research in Science and Technology. 4(11). 247–250. https://doi.org/10.32628/ijsrst18401146
Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review. 56(8). 8219–8264. https://doi.org/10.1007/s10462-022-10366-3
Rinaldi, A. M., & Russo, C. (2021). Using a multimedia semantic graph for web document visualization and summarization. Multimedia Tools and Applications. 80(3). 3885–3925.
Safna, F. F., & Apriantoro, M. S. (2024). Exploring the Effectiveness of BWI’s Digital Waqf Campaign: A Netnographic Analysis of Social Media Engagement. Ethica: International Journal of Humanities and Social Science Studies. 2(1). 1-20.
Schwarz, C. (2018). Ldagibbs: A command for topic modeling in stata using latent Dirichlet allocation. Stata Journal. 18(1). 101–117. https://doi.org/10.1177/1536867x1801800107
Takes, F. W., & Heemskerk, E. M. (2016). Centrality in the global network of corporate control. Social Network Analysis and Mining. 6(1). 1–18. https://doi.org/10.1007/s13278-016-0402-5
Tiwari, D., Nagpal, B., Bhati, B. S., Mishra, A., & Kumar, M. (2023). A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques. Artificial Intelligence Review Springer Netherlands. 56(11). https://doi.org/10.1007/s10462-023-10472-w
Ulfah, I. F., Sukmana, R., Laila, N., & Sulaeman, S. (2024). A structured literature review on green sukuk (Islamic bonds): implications for government policy and future studies. Journal of Islamic Accounting and Business Research. 15(7). 1118–1133. https://doi.org/10.1108/JIABR-10-2022-0255
Vasiliauskaite, V., & Rosas, F. E. (2020). Understanding complexity via network theory: a gentle introduction. 1–19. http://arxiv.org/abs/2004.14845
Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems. 94. 101582. https://doi.org/10.1016/j.is.2020.101582
Widianto, A., Pebriyanto, E., Fitriyanti, F., & Marna, M. (2024). Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity. Journal of Dinda : Data Science, Information Technology, and Data Analytics. 4. 149–153. https://doi.org/10.20895/dinda.v4i2.1589
Wiratama, B. F., & Safitri, Z. (2023). Is Islamic Economy as that Green?. Journal Al-Muzara’Ah. 11(1). 103–118. https://doi.org/10.29244/jam.11.1.103-118
Yang, Y., Li, J., Shen, D., Nan, M., & Cui, Q. (2018). Evolutionary dynamics analysis of complex network with fusion nodes and overlap edges. Journal of Systems Engineering and Electronics. 29(3). 549-559. https://doi.org/10.21629/JSEE.2018.03.12
Zhao, F., Ren, X., Yang, S., Han, Q., Zhao, P., & Yang, X. (2021). Latent Dirichlet Allocation Model Training with Differential Privacy. IEEE Transactions on Information Forensics and Security. 16. 1290–1305. https://doi.org/10.1109/TIFS.2020.3032021
Zumbraegel, T. (2022). Inside the green state: Institutional organization, agency and governance. In Political power and environmental sustainability in gulf monarchies. Springer Nature. https://link.springer.com/chapter/10.1007/978-981-19-4431-4_1
Authors
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