Kuy Learn to Pray Application to Train the Practice of Prayer in Children
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Development, Application, Learning to Pray
Abstract
Background. This research aims to create an android application-based prayer learning media product to train and teach prayer to children. This research uses a type of development research or known as Research & Development (R&D).
Purpose. The development model used in this research refers to the Borg and Gall development model.
Method. The research process begins with a preliminary study to see the problems that occur, the potential for development, determining the literature review relevant to the problems that occur to plan the manufacture of the initial product design, initial product field trials.
Results. , product revision I, main field trials, product revision II, operational field trials.
Conclusion. final product revision and dissemination and implementation of product application. The results of the research on the use of the Kuy Belajar Shalat Application developed in this study with a series of steps and stages of trials can be said to be effective for training children in learning prayers because there is an increase in scores in each trial conducted
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Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., & Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76, 243–297. https://doi.org/10.1016/j.inffus.2021.05.008
Abdelhamid, H. N. (2021). A review on hydrogen generation from the hydrolysis of sodium borohydride. International Journal of Hydrogen Energy, 46(1), 726–765. https://doi.org/10.1016/j.ijhydene.2020.09.186
Aich, S., Chakraborty, S., Sain, M., Lee, H., & Kim, H.-C. (2019). A Review on Benefits of IoT Integrated Blockchain based Supply Chain Management Implementations across Different Sectors with Case Study. 2019 21st International Conference on Advanced Communication Technology (ICACT), 138–141. https://doi.org/10.23919/ICACT.2019.8701910
Ali, F., El-Sappagh, S., Islam, S. M. R., Kwak, D., Ali, A., Imran, M., & Kwak, K.-S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208–222. https://doi.org/10.1016/j.inffus.2020.06.008
Alkhateeb, J. H. (2020). A Machine Learning Approach for Recognizing the Holy Quran Reciter. International Journal of Advanced Computer Science and Applications, 11(7). https://doi.org/10.14569/IJACSA.2020.0110735
Alzoubi, H., Alshurideh, M., Kurdi, B. A., Akour, I., & Azi, R. (2022). Does BLE technology contribute towards improving marketing strategies, customers’ satisfaction and loyalty? The role of open innovation. International Journal of Data and Network Science, 6(2), 449–460. https://doi.org/10.5267/j.ijdns.2021.12.009
Ante, L. (2021). Blockchain and energy: A bibliometric analysis and review. Renewable and Sustainable Energy Reviews, 137(Query date: 2023-06-08 17:04:34). https://doi.org/10.1016/j.rser.2020.110597
Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019). A Systematic Review of Studies on Educational Robotics. Journal of Pre-College Engineering Education Research (J-PEER), 9(2). https://doi.org/10.7771/2157-9288.1223
Caena, F., & Redecker, C. (2019). Aligning teacher competence frameworks to 21st century challenges: The case for the European Digital Competence Framework for Educators ( DIGCOMPEDU) . European Journal of Education, 54(3), 356–369. https://doi.org/10.1111/ejed.12345
Cao, B., Wang, Y., Wen, D., Liu, W., Wang, J., Fan, G., Ruan, L., Song, B., Cai, Y., Wei, M., Li, X., Xia, J., Chen, N., Xiang, J., Yu, T., Bai, T., Xie, X., Zhang, L., Li, C., … Wang, C. (2020). A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19. New England Journal of Medicine, 382(19), 1787–1799. https://doi.org/10.1056/NEJMoa2001282
Cerezo, R., Calderón, V., & Romero, C. (2019). A holographic mobile-based application for practicing pronunciation of basic English vocabulary for Spanish speaking children. International Journal of Human-Computer Studies, 124, 13–25. https://doi.org/10.1016/j.ijhcs.2018.11.009
Chen, L.-K., Woo, J., Assantachai, P., Auyeung, T.-W., Chou, M.-Y., Iijima, K., Jang, H. C., Kang, L., Kim, M., Kim, S., Kojima, T., Kuzuya, M., Lee, J. S. W., Lee, S. Y., Lee, W.-J., Lee, Y., Liang, C.-K., Lim, J.-Y., Lim, W. S., … Arai, H. (2020). Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Journal of the American Medical Directors Association, 21(3), 300-307.e2. https://doi.org/10.1016/j.jamda.2019.12.012
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039–3071. https://doi.org/10.1109/COMST.2019.2926625
Coutts, D. S., Matthews, W. A., & Hubbard, S. M. (2019). Assessment of widely used methods to derive depositional ages from detrital zircon populations. Geoscience Frontiers, 10(4), 1421–1435. https://doi.org/10.1016/j.gsf.2018.11.002
Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., & Bray, F. (2021). Cancer statistics for the year 2020: An overview. International Journal of Cancer, 149(4), 778–789. https://doi.org/10.1002/ijc.33588
Germani, L., Mecarelli, V., Baruffa, G., Rugini, L., & Frescura, F. (2019). An IoT Architecture for Continuous Livestock Monitoring Using LoRa LPWAN. Electronics, 8(12), 1435. https://doi.org/10.3390/electronics8121435
Hamzah, N., Abd Halim, N. D., Hassan, M. H., & Ariffin, A. (2019). Android Application for Children to Learn Basic Solat. International Journal of Interactive Mobile Technologies (iJIM), 13(07), 69. https://doi.org/10.3991/ijim.v13i07.10758
Heidenreich, P. A., Bozkurt, B., Aguilar, D., Allen, L. A., Byun, J. J., Colvin, M. M., Deswal, A., Drazner, M. H., Dunlay, S. M., Evers, L. R., Fang, J. C., Fedson, S. E., Fonarow, G. C., Hayek, S. S., Hernandez, A. F., Khazanie, P., Kittleson, M. M., Lee, C. S., Link, M. S., … Yancy, C. W. (2022). 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 145(18). https://doi.org/10.1161/CIR.0000000000001063
Magnavita, N., Soave, P. M., & Antonelli, M. (2021). A One-Year Prospective Study of Work-Related Mental Health in the Intensivists of a COVID-19 Hub Hospital. International Journal of Environmental Research and Public Health, 18(18), 9888. https://doi.org/10.3390/ijerph18189888
Marshall, J. C., Murthy, S., Diaz, J., Adhikari, N. K., Angus, D. C., Arabi, Y. M., Baillie, K., Bauer, M., Berry, S., Blackwood, B., Bonten, M., Bozza, F., Brunkhorst, F., Cheng, A., Clarke, M., Dat, V. Q., De Jong, M., Denholm, J., Derde, L., … Zhang, J. (2020). A minimal common outcome measure set for COVID-19 clinical research. The Lancet Infectious Diseases, 20(8), e192–e197. https://doi.org/10.1016/S1473-3099(20)30483-7
Mazza, C., Ricci, E., Biondi, S., Colasanti, M., Ferracuti, S., Napoli, C., & Roma, P. (2020). A Nationwide Survey of Psychological Distress among Italian People during the COVID-19 Pandemic: Immediate Psychological Responses and Associated Factors. International Journal of Environmental Research and Public Health, 17(9), 3165. https://doi.org/10.3390/ijerph17093165
Mohammed, A., Harris, I., & Govindan, K. (2019). A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. International Journal of Production Economics, 217, 171–184. https://doi.org/10.1016/j.ijpe.2019.02.003
Mulangu, S., Dodd, L. E., Davey, R. T., Tshiani Mbaya, O., Proschan, M., Mukadi, D., Lusakibanza Manzo, M., Nzolo, D., Tshomba Oloma, A., Ibanda, A., Ali, R., Coulibaly, S., Levine, A. C., Grais, R., Diaz, J., Lane, H. C., Muyembe-Tamfum, J.-J., & The Palm Writing Group. (2019). A Randomized, Controlled Trial of Ebola Virus Disease Therapeutics. New England Journal of Medicine, 381(24), 2293–2303. https://doi.org/10.1056/NEJMoa1910993
Natarajan, Y., Murugesan, P. K., Mohan, M., & Liyakath Ali Khan, S. A. (2020). Abrasive Water Jet Machining process: A state of art of review. Journal of Manufacturing Processes, 49, 271–322. https://doi.org/10.1016/j.jmapro.2019.11.030
Paradis, E., & Schliep, K. (2019). ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35(3), 526–528. https://doi.org/10.1093/bioinformatics/bty633
Rahimzadeh, M., Attar, A., & Sakhaei, S. M. (2021). A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomedical Signal Processing and Control, 68, 102588. https://doi.org/10.1016/j.bspc.2021.102588
Ren, Z., Sun, S., Sun, R., Cui, G., Hong, L., Rao, B., Li, A., Yu, Z., Kan, Q., & Mao, Z. (2020). A Metal–Polyphenol?Coordinated Nanomedicine for Synergistic Cascade Cancer Chemotherapy and Chemodynamic Therapy. Advanced Materials, 32(6), 1906024. https://doi.org/10.1002/adma.201906024
Shi, X.-L., Zou, J., & Chen, Z.-G. (2020). Advanced Thermoelectric Design: From Materials and Structures to Devices. Chemical Reviews, 120(15), 7399–7515. https://doi.org/10.1021/acs.chemrev.0c00026
Tang, N., Li, D., Wang, X., & Sun, Z. (2020). Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. Journal of Thrombosis and Haemostasis, 18(4), 844–847. https://doi.org/10.1111/jth.14768
Tu, Y.-F., Chien, C.-S., Yarmishyn, A. A., Lin, Y.-Y., Luo, Y.-H., Lin, Y.-T., Lai, W.-Y., Yang, D.-M., Chou, S.-J., Yang, Y.-P., Wang, M.-L., & Chiou, S.-H. (2020). A Review of SARS-CoV-2 and the Ongoing Clinical Trials. International Journal of Molecular Sciences, 21(7), 2657. https://doi.org/10.3390/ijms21072657
Tulbure, A.-A., Tulbure, A.-A., & Dulf, E.-H. (2022). A review on modern defect detection models using DCNNs – Deep convolutional neural networks. Journal of Advanced Research, 35, 33–48. https://doi.org/10.1016/j.jare.2021.03.015
Twenge, J. M., Cooper, A. B., Joiner, T. E., Duffy, M. E., & Binau, S. G. (2019). Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005–2017. Journal of Abnormal Psychology, 128(3), 185–199. https://doi.org/10.1037/abn0000410
Verdoni, L., Mazza, A., Gervasoni, A., Martelli, L., Ruggeri, M., Ciuffreda, M., Bonanomi, E., & D’Antiga, L. (2020). An outbreak of severe Kawasaki-like disease at the Italian epicentre of the SARS-CoV-2 epidemic: An observational cohort study. The Lancet, 395(10239), 1771–1778. https://doi.org/10.1016/S0140-6736(20)31103-X
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019). Adaptive Federated Learning in Resource Constrained Edge Computing Systems. IEEE Journal on Selected Areas in Communications, 37(6), 1205–1221. https://doi.org/10.1109/JSAC.2019.2904348
West, H., McCleod, M., Hussein, M., Morabito, A., Rittmeyer, A., Conter, H. J., Kopp, H.-G., Daniel, D., McCune, S., Mekhail, T., Zer, A., Reinmuth, N., Sadiq, A., Sandler, A., Lin, W., Ochi Lohmann, T., Archer, V., Wang, L., Kowanetz, M., & Cappuzzo, F. (2019). Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): A multicentre, randomised, open-label, phase 3 trial. The Lancet Oncology, 20(7), 924–937. https://doi.org/10.1016/S1470-2045(19)30167-6
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., & Ma, K. (2019). Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 3712–3721. https://doi.org/10.1109/ICCV.2019.00381
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Copyright (c) 2023 Ryan Apriansyah, Hamdani Ali Mukti, Imam Tabroni
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