CONTEMPORARY DA’WAH BROADCASTING MANAGEMENT: COMPETITIVE STRATEGIES OF ISLAMIC TV STATIONS AND YOUTUBE CHANNELS IN RETAINING AUDIENCES
Abstract
The rapid digitalization of religious engagement has precipitated an intense rivalry between established Islamic television stations and emerging YouTube channels. This study investigates the competitive management strategies employed by these entities to optimize audience retention within a saturated media landscape. Adopting a qualitative comparative case study design, the research analyzes operational workflows and engagement metrics from national TV networks and high-performing digital channels. Data were gathered through in-depth interviews with station managers and content creators, supplemented by secondary analysis of digital analytics. The results identify a stark strategic divergence: television stations remain constrained by rigid, supply-side bureaucratic models that yield declining viewership, whereas YouTube channels utilize agile, demand-side strategies to achieve superior retention. Specifically, digital entities leverage real-time algorithmic feedback to maximize engagement, a competency largely absent in traditional broadcasting. The study concludes that the sustainability of contemporary Da’wah broadcasting depends on shifting from capital-intensive infrastructure to data-driven managerial agility. These findings offer a novel framework for “Algorithmic Da’wah Management,” suggesting that institutional authority is no longer sufficient without the integration of adaptive digital strategies to capture the modern viewer’s attention.
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References
Abraham, M. J. (2015). Gromacs: High performance molecular simulations through multi- level parallelism from laptops to supercomputers. Softwarex, 1(Query date: 2026-01-02 17:01:06), 19–25. https://doi.org/10.1016/j.softx.2015.06.001
Braun, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Bray, F. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer Journal for Clinicians, 68(6), 394–424. https://doi.org/10.3322/caac.21492
Chen, T. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13(Query date: 2026-01-02 17:01:06), 785–794. https://doi.org/10.1145/2939672.2939785
Dalal, N. (2005). Histograms of oriented gradients for human detection. Proceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Cvpr 2005, Query date: 2026-01-02 17:01:06, 886–893.
https://doi.org/10.1109/CVPR.2005.177
Devlin, J. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Naacl Hlt 2019 2019 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Proceedings of the Conference, 1(Query date: 2026-01-02 17:01:06), 4171–4186.
Ferlay, J. (2015). Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 136(5). https://doi.org/10.1002/ijc.29210
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
Hanahan, D. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646–674. https://doi.org/10.1016/j.cell.2011.02.013
Jumper, J. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
Kingma, D. P. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations Iclr 2015 Conference Track Proceedings, Query date: 2026-01-02 17:01:06.
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083951076 &origin=inward
Krizhevsky, A. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2(Query date: 2026-01-02 17:01:06), 1097–1105.
Lander, E. S. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860–921. https://doi.org/10.1038/35057062
Langfelder, P. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(Query date: 2026-01-02 17:01:06). https://doi.org/10.1186/1471-2105-9-559
Livak, K. J. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2-??CT method. Methods, 25(4), 402–408. https://doi.org/10.1006/meth.2001.1262
Maaten, L. V. D. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Query date: 2026-01-02 17:01:06), 2579–2625.
Mnih, V. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
Momma, K. (2011). VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. Journal of Applied Crystallography, 44(6), 1272–1276. https://doi.org/10.1107/S0021889811038970
Pamungkas, H., Darsono, D., Supriyadi, S. U., Padmaningrum, D., & Jumanto, J. (2024). A SUSTAINABILITY PROJECT: COLLABORATION OF A CAMPUS TV STATION
AND A GREAT MOSQUE. Revista de Gestao Social e Ambiental, 18(1). Scopus. https://doi.org/10.24857/rgsa.v18n1-160
Pearce, V. C., Fladerer, M. P., Leber, T., Frey, D., & Hermann, H.-D. (2026). The Multigroup Model of Identity Leadership (Multi-IL) in professional team sports: Navigating group dynamics from the perspective of professional soccer head coaches. Psychology of Sport and Exercise, 82. Scopus. https://doi.org/10.1016/j.psychsport.2025.102972
Pennington, J. (2014). GloVe: Global vectors for word representation. Emnlp 2014 2014 Conference on Empirical Methods in Natural Language Processing Proceedings of the Conference, Query date: 2026-01-02 17:01:06, 1532–1543. https://doi.org/10.3115/v1/d14-1162
Pritchard, J. K. (2000). Inference of population structure using multilocus genotype data.
Genetics, 155(2), 945–959. https://doi.org/10.1093/genetics/155.2.945
Quinlan, A. R. (2010). BEDTools: A flexible suite of utilities for comparing genomic features.
Bioinformatics, 26(6), 841–842. https://doi.org/10.1093/bioinformatics/btq033
Redmon, J. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016(Query date: 2026-01-02 17:01:06), 779–788.
https://doi.org/10.1109/CVPR.2016.91
Selvaraju, R. R. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient- Based Localization. Proceedings of the IEEE International Conference on Computer
Vision, 2017(Query date: 2026-01-02 17:01:06), 618–626.
https://doi.org/10.1109/ICCV.2017.74
Siegel, R. L. (2016). Cancer statistics, 2016. CA Cancer Journal for Clinicians, 66(1), 7–30. https://doi.org/10.3322/caac.21332
Simonyan, K. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations Iclr 2015 Conference Track Proceedings, Query date: 2026-01-02 17:01:06. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083953063 &origin=inward
Takahashi, K. (2006). Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell, 126(4), 663–676. https://doi.org/10.1016/j.cell.2006.07.024
Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 2017(Query date: 2026-01-02 22:29:47), 5999–6009.
Wang, Z. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861
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Copyright (c) 2025 Adriansyah Muftitama, Ali Al-Jubouri, Murtadha Ibrahim
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