Utilization of the Kinemaster Application in Supporting Video-Based Islamic Jurisprudence Learning

Authors

  • Martias Martias Universitas Islam Negeri Imam Bonjol Padang
  • Aiman Fikri STAI Rahmaniyah Sekayu
  • Nur Afni Oktavia Mahmud Yunus State Islamic University Batusangkar
  • Vann Sok International University

DOI:

https://doi.org/10.55849/attasyrih.v10i2.270

Keywords:

Kinemaster, Video Media, Learning Outcomes

Abstract

In the current era of globalization, the use of technology media in the midst of society has a very important position. The results of technology media are difficult to separate from everyday life because they greatly help human needs, one of which is the teaching and learning process in the world of education. In addition, the researcher intends to find out the needs of a teacher in learning fiqh by means of the audio-visual method using the kinmaster application. This research was conducted using a quantitative method using survey capital and an in-depth survey by dividing google from. The results of the study are that the kinmaster application can be used by teachers as an alternative in carrying out fiqh learning so that they are able to understand the material effectively and clearly. For this reason, in order to facilitate the use of the kinmaster application, teachers should be proficient in using technology media because the material to be given is through the teacher concerned. So far, the researcher's limitation is that the researcher cannot know for sure whether the teachers have given individual assignments to students for the implementation of fiqh material. Therefore, the researcher can conduct further research aimed at students practicing fiqh learning in their lives.

 

References

Abdel-Mageed, A. M., Rungtaweevoranit, B., Parlinska-Wojtan, M., Pei, X., Yaghi, O. M., & Behm, R. J. (2019). Highly Active and Stable Single-Atom Cu Catalysts Supported by a Metal–Organic Framework. Journal of the American Chemical Society, 141(13), 5201–5210. https://doi.org/10.1021/jacs.8b11386

Abedinia, O., Amjady, N., & Ghadimi, N. (2018). Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Computational Intelligence, 34(1), 241–260. https://doi.org/10.1111/coin.12145

Akseer, N., Kandru, G., Keats, E. C., & Bhutta, Z. A. (2020). COVID-19 pandemic and mitigation strategies: Implications for maternal and child health and nutrition. The American Journal of Clinical Nutrition, 112(2), 251–256. https://doi.org/10.1093/ajcn/nqaa171

Amjad, M., Rehmani, M. H., & Mao, S. (2018). Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 20(2), 1056–1103. https://doi.org/10.1109/COMST.2018.2794358

Barakabitze, A. A., Ahmad, A., Mijumbi, R., & Hines, A. (2020). 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, 167, 106984. https://doi.org/10.1016/j.comnet.2019.106984

Bond, M. (2020). Facilitating student engagement through the flipped learning approach in K-12: A systematic review. Computers & Education, 151, 103819. https://doi.org/10.1016/j.compedu.2020.103819

Brauner, J. M., Mindermann, S., Sharma, M., Johnston, D., Salvatier, J., Gaven?iak, T., Stephenson, A. B., Leech, G., Altman, G., Mikulik, V., Norman, A. J., Monrad, J. T., Besiroglu, T., Ge, H., Hartwick, M. A., Teh, Y. W., Chindelevitch, L., Gal, Y., & Kulveit, J. (2021). Inferring the effectiveness of government interventions against COVID-19. Science, 371(6531), eabd9338. https://doi.org/10.1126/science.abd9338

Bretag, T., Harper, R., Burton, M., Ellis, C., Newton, P., Rozenberg, P., Saddiqui, S., & van Haeringen, K. (2019). Contract cheating: A survey of Australian university students. Studies in Higher Education, 44(11), 1837–1856. https://doi.org/10.1080/03075079.2018.1462788

Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., Welch, V., & Thomson, H. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ, l6890. https://doi.org/10.1136/bmj.l6890

Carr, M., Haar, A., Amores, J., Lopes, P., Bernal, G., Vega, T., Rosello, O., Jain, A., & Maes, P. (2020). Dream engineering: Simulating worlds through sensory stimulation. Consciousness and Cognition, 83, 102955. Dampak_Pandemi_Covid-19_Terhadap_Aktivitas_Belajar_Siswa_di_Madrasah_Ibtidaiyah[1].doc

Chan, J. F.-W., Yuan, S., Kok, K.-H., To, K. K.-W., Chu, H., Yang, J., Xing, F., Liu, J., Yip, C. C.-Y., Poon, R. W.-S., Tsoi, H.-W., Lo, S. K.-F., Chan, K.-H., Poon, V. K.-M., Chan, W.-M., Ip, J. D., Cai, J.-P., Cheng, V. C.-C., Chen, H., … Yuen, K.-Y. (2020). A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. The Lancet, 395(10223), 514–523. https://doi.org/10.1016/S0140-6736(20)30154-9

Chen, M., Wei, L., Law, C.-T., Tsang, F. H.-C., Shen, J., Cheng, C. L.-H., Tsang, L.-H., Ho, D. W.-H., Chiu, D. K.-C., Lee, J. M.-F., Wong, C. C.-L., Ng, I. O.-L., & Wong, C.-M. (2018). RNA N6-methyladenosine methyltransferase-like 3 promotes liver cancer progression through YTHDF2-dependent posttranscriptional silencing of SOCS2. Hepatology, 67(6), 2254–2270. https://doi.org/10.1002/hep.29683

Dong, Y., Mo, X., Hu, Y., Qi, X., Jiang, F., Jiang, Z., & Tong, S. (2020). Epidemiology of COVID-19 Among Children in China. Pediatrics, 145(6), e20200702. https://doi.org/10.1542/peds.2020-0702

Duan, X., Sun, H., & Wang, S. (2018). Metal-Free Carbocatalysis in Advanced Oxidation Reactions. Accounts of Chemical Research, 51(3), 678–687. https://doi.org/10.1021/acs.accounts.7b00535

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 55, 102211. https://doi.org/10.1016/j.ijinfomgt.2020.102211

Hank, C., Gelpke, S., Schnabl, A., White, R. J., Full, J., Wiebe, N., Smolinka, T., Schaadt, A., Henning, H.-M., & Hebling, C. (2018). Economics & carbon dioxide avoidance cost of methanol production based on renewable hydrogen and recycled carbon dioxide – power-to-methanol. Sustainable Energy & Fuels, 2(6), 1244–1261. https://doi.org/10.1039/C8SE00032H

Herman, K. C., Hickmon-Rosa, J., & Reinke, W. M. (2018). Empirically Derived Profiles of Teacher Stress, Burnout, Self-Efficacy, and Coping and Associated Student Outcomes. Journal of Positive Behavior Interventions, 20(2), 90–100. https://doi.org/10.1177/1098300717732066

Huang, C., Huang, L., Wang, Y., Li, X., Ren, L., Gu, X., Kang, L., Guo, L., Liu, M., Zhou, X., Luo, J., Huang, Z., Tu, S., Zhao, Y., Chen, L., Xu, D., Li, Y., Li, C., Peng, L., … Cao, B. (2021). 6-month consequences of COVID-19 in patients discharged from hospital: A cohort study. The Lancet, 397(10270), 220–232. https://doi.org/10.1016/S0140-6736(20)32656-8

Kang, S., & Kim, Y. (2021). Examining the quality of mobile-assisted, video-making task outcomes: The role of proficiency, narrative ability, digital literacy, and motivation. Language Teaching Research, 136216882110479. https://doi.org/10.1177/13621688211047984

Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831. https://doi.org/10.1016/j.ijpe.2020.107831

Kraft, M. A., Blazar, D., & Hogan, D. (2018). The Effect of Teacher Coaching on Instruction and Achievement: A Meta-Analysis of the Causal Evidence. Review of Educational Research, 88(4), 547–588. https://doi.org/10.3102/0034654318759268

Liang, W., Liang, H., Ou, L., Chen, B., Chen, A., Li, C., Li, Y., Guan, W., Sang, L., Lu, J., Xu, Y., Chen, G., Guo, H., Guo, J., Chen, Z., Zhao, Y., Li, S., Zhang, N., Zhong, N., … for the China Medical Treatment Expert Group for COVID-19. (2020). Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19. JAMA Internal Medicine, 180(8), 1081. https://doi.org/10.1001/jamainternmed.2020.2033

Liu, W., Li, X., Wang, C., Pan, H., Liu, W., Wang, K., Zeng, Q., Wang, R., & Jiang, J. (2019). A Scalable General Synthetic Approach toward Ultrathin Imine-Linked Two-Dimensional Covalent Organic Framework Nanosheets for Photocatalytic CO 2 Reduction. Journal of the American Chemical Society, 141(43), 17431–17440. https://doi.org/10.1021/jacs.9b09502

Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., Bi, Y., Ma, X., Zhan, F., Wang, L., Hu, T., Zhou, H., Hu, Z., Zhou, W., Zhao, L., … Tan, W. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. The Lancet, 395(10224), 565–574. https://doi.org/10.1016/S0140-6736(20)30251-8

Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J.-X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., Worden, J. R., Janssens-Maenhout, G., & Parker, R. J. (2019). Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. Atmospheric Chemistry and Physics, 19(11), 7859–7881. https://doi.org/10.5194/acp-19-7859-2019

Macaro, E., Curle, S., Pun, J., An, J., & Dearden, J. (2018). A systematic review of English medium instruction in higher education. Language Teaching, 51(1), 36–76. https://doi.org/10.1017/S0261444817000350

Mishra, L., Gupta, T., & Shree, A. (2020). Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. International Journal of Educational Research Open, 1, 100012. https://doi.org/10.1016/j.ijedro.2020.100012

Palanisamy, N., Öztürk, M. A., Akmeriç, E. B., & Di Ventura, B. (2020). C-terminal eYFP fusion impairs Escherichia coli MinE function. Open Biology, 10(5), 200010. https://doi.org/10.1098/rsob.200010

Pratt, R. G. (1999). Seismic waveform inversion in the frequency domain, Part 1: Theory and verification in a physical scale model. GEOPHYSICS, 64(3), 888–901. https://doi.org/10.1190/1.1444597

Qi, P., Cao, J., Yang, T., Guo, J., & Li, J. (2019). Exploiting Multi-domain Visual Information for Fake News Detection. 2019 IEEE International Conference on Data Mining (ICDM), 518–527. https://doi.org/10.1109/ICDM.2019.00062

Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N., & Rueckert, D. (2018). A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging, 37(2), 491–503. https://doi.org/10.1109/TMI.2017.2760978

Schneider, L. (2020). A resurrection of aducanumab for Alzheimer’s disease. The Lancet Neurology, 19(2), 111–112. https://doi.org/10.1016/S1474-4422(19)30480-6

Ståhl, A., Tsaknaki, V., & Balaam, M. (2021). Validity and Rigour in Soma Design-Sketching with the Soma. ACM Transactions on Computer-Human Interaction, 28(6), 1–36. https://doi.org/10.1145/3470132

Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., & Richardson, P. (2020). COVID-19: Combining antiviral and anti-inflammatory treatments. The Lancet Infectious Diseases, 20(4), 400–402. https://doi.org/10.1016/S1473-3099(20)30132-8

Tan, B. Y. Q., Chew, N. W. S., Lee, G. K. H., Jing, M., Goh, Y., Yeo, L. L. L., Zhang, K., Chin, H.-K., Ahmad, A., Khan, F. A., Shanmugam, G. N., Chan, B. P. L., Sunny, S., Chandra, B., Ong, J. J. Y., Paliwal, P. R., Wong, L. Y. H., Sagayanathan, R., Chen, J. T., … Sharma, V. K. (2020). Psychological Impact of the COVID-19 Pandemic on Health Care Workers in Singapore. Annals of Internal Medicine, 173(4), 317–320. https://doi.org/10.7326/M20-1083

Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., … Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476–6483. https://doi.org/10.1073/pnas.1916903117

Tjoa, E., & Guan, C. (2021). A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314

Ton, A., Gentile, F., Hsing, M., Ban, F., & Cherkasov, A. (2020). Rapid Identification of Potential Inhibitors of SARS?CoV?2 Main Protease by Deep Docking of 1.3 Billion Compounds. Molecular Informatics, 39(8), 2000028. https://doi.org/10.1002/minf.202000028

Tonetti, M. S., & Sanz, M. (2019). Implementation of the new classification of periodontal diseases: Decision?making algorithms for clinical practice and education. Journal of Clinical Periodontology, 46(4), 398–405. https://doi.org/10.1111/jcpe.13104

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850

Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., & Chandraker, M. (2018). Learning to Adapt Structured Output Space for Semantic Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7472–7481. https://doi.org/10.1109/CVPR.2018.00780

Wang, S., Cai, L.-M., Wen, H.-H., Luo, J., Wang, Q.-S., & Liu, X. (2019). Spatial distribution and source apportionment of heavy metals in soil from a typical county-level city of Guangdong Province, China. Science of The Total Environment, 655, 92–101. https://doi.org/10.1016/j.scitotenv.2018.11.244

Wang, S., Xu, J., Wang, W., Wang, G.-J. N., Rastak, R., Molina-Lopez, F., Chung, J. W., Niu, S., Feig, V. R., Lopez, J., Lei, T., Kwon, S.-K., Kim, Y., Foudeh, A. M., Ehrlich, A., Gasperini, A., Yun, Y., Murmann, B., Tok, J. B.-H., & Bao, Z. (2018). Skin electronics from scalable fabrication of an intrinsically stretchable transistor array. Nature, 555(7694), 83–88. https://doi.org/10.1038/nature25494

Xie, X., Huo, J., & Zou, H. (2019). Green process innovation, green product innovation, and corporate financial performance: A content analysis method. Journal of Business Research, 101, 697–706. https://doi.org/10.1016/j.jbusres.2019.01.010

Xu, X., Han, M., Li, T., Sun, W., Wang, D., Fu, B., Zhou, Y., Zheng, X., Yang, Y., Li, X., Zhang, X., Pan, A., & Wei, H. (2020). Effective treatment of severe COVID-19 patients with tocilizumab. Proceedings of the National Academy of Sciences, 117(20), 10970–10975. https://doi.org/10.1073/pnas.2005615117

Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLOS ONE, 15(9), e0239441. https://doi.org/10.1371/journal.pone.0239441

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., & Cao, B. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. The Lancet, 395(10229), 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3

Downloads

Published

2025-03-13

How to Cite

Martias, M., Fikri, A., Oktavia, N. A., & Sok, V. (2025). Utilization of the Kinemaster Application in Supporting Video-Based Islamic Jurisprudence Learning. At-Tasyrih: Jurnal Pendidikan Dan Hukum Islam, 10(2), 494–508. https://doi.org/10.55849/attasyrih.v10i2.270