Utilization of Animiz Animation Application in Arabic Class 2 Madrasah Tsanawiyah Lessons at Diniyyah Pasia Modern Islamic Boarding School
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Animiz Animation Aplication, Arab, Diniyyah Pasia
Abstract
This study uses a qualitative method. With this method, the researcher conducted interviews with some members of the second class of private madrasah tsanawiyyah at the modern Islamic boarding school Diniyyah Pasia, Agam. This Animiz Animation application is one of the animation media-based applications that really helps students to understand the presentation of material explained using moving images which increases the interest and interest of everyone to listen carefully to the explanations in the video and understand and understand the explanations provided. There by looking at the animation created. The benefit of this application is that it makes it easier for educators to convey the subject matter they want to convey and also for students, with this application educators do not need to carry out teaching and learning activities with the conventional system, namely face to face especially during the covid-19 pandemic. With this application, information and communication technology is also developing because if you use this application often, knowledge about this application will be spread and can be increased to a higher and sophisticated level. By conducting this research, it is also hoped that learning media that were previously unknown will become known because of the practicality of understanding using animation media because it has described the purposes of the explanations presented in the video presentations made. Educational technology using advances in information and confirmation will increase if knowledge about it is growing. Then the educational media becomes more sophisticated and practical and makes the education system in Indonesia more developed and advanced.
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References
Abdeen, M. A. R. (2019). A closer look at arabic text classification. International Journal of Advanced Computer Science and Applications, 10(11), 677–688. https://doi.org/10.14569/IJACSA.2019.0101189
AbuZeina, D. (2018). Employing fisher discriminant analysis for Arabic text classification. Computers and Electrical Engineering, 66, 474–486. https://doi.org/10.1016/j.compeleceng.2017.11.002
Addi, H. A. (2020). Three-level binary tree structure for sentiment classification in Arabic text. In ACM International Conference Proceeding Series. https://doi.org/10.1145/3386723.3387844
Aflisia, N., & Harahap, P. (2019). Eksisten Bi’ah Lughawiyah Sebagai Media Berbahasa Arab dalam Meningkatkan Kemampuan Muhadatsah Mahasiswa Prodi Pendidikan Bahasa Arab IAIN Curup. Lisanul’Arab: Journal of Arabic …. http://repository.iaincurup.ac.id/38/
Ahmad, R. (2020). A deep learning based arabic script recognition system: Benchmark on khat. International Arab Journal of Information Technology, 17(3), 299–305. https://doi.org/10.34028/iajit/17/3/3
Ahmed, M. A. (2019). The classification of the modern Arabic poetry using machine learning. Telkomnika (Telecommunication Computing Electronics and Control), 17(5), 2667–2674. https://doi.org/10.12928/TELKOMNIKA.v17i5.12646
Al-sheyab, N. A. (2018). The effect of a “class smoke free pledge” on breath carbon monoxide in Arabic male adolescents. Nicotine and Tobacco Research, 20(5), 568–574. https://doi.org/10.1093/ntr/ntx050
Al-Yahya, M. (2019). A comparative study of machine learning methods for genre identification of classical Arabic text. Computers, Materials and Continua, 60(2), 421–433. https://doi.org/10.32604/cmc.2019.06209
Amin, F. M., & Sundari, H. (2020). Efl students’ preferences on digital platforms during emergency remote teaching: Video conference, lms, or messenger application? Studies in English Language and Education, 7(2), 362–378. https://doi.org/10.24815/siele.v7i2.16929
Amin, T. (2020). Teaching science in Arabic: diglossia and discourse patterns in the elementary classroom. International Journal of Science Education, 42(14), 2290–2330. https://doi.org/10.1080/09500693.2019.1629039
Boudad, N. (2020). Exploring the Use of Word Embedding and Deep Learning in Arabic Sentiment Analysis. In Advances in Intelligent Systems and Computing (Vol. 1105, pp. 243–253). https://doi.org/10.1007/978-3-030-36674-2_26
Boukil, S. (2018). Arabic Stemming techniques as feature extraction applied in Arabic text classification. In Lecture Notes in Networks and Systems (Vol. 25, pp. 349–361). https://doi.org/10.1007/978-3-319-69137-4_31
El-Alami, F. z. (2021). Contextual semantic embeddings based on fine-tuned AraBERT model for Arabic text multi-class categorization. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.02.005
Gridach, M. (2018). Deep learning approach for arabic named entity recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9623, pp. 439–451). https://doi.org/10.1007/978-3-319-75477-2_31
Guru, D. (2018). A Novel Term Weighting Scheme and an Approach for Classification of Agricultural Arabic Text Complaints. In 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 (pp. 24–28). https://doi.org/10.1109/ASAR.2018.8480317
Hassanein, A. M. D. E. (2019). A proposed model of selecting features for classifying Arabic text. Jordanian Journal of Computers and Information Technology, 5(3), 275–290. https://doi.org/10.5455/jjcit.71-1564059469
Hisham, B. (2019). Supervised learning classifiers for Arabic gestures recognition using Kinect V2. SN Applied Sciences, 1(7). https://doi.org/10.1007/s42452-019-0771-2
Khalaf, Z. A. (2018). Filtering approach and system combination for Arabic news classification. Journal of Theoretical and Applied Information Technology, 96(14), 4491–4501. https://api.elsevier.com/content/abstract/scopus_id/85051138783
M. Khalilullah, S. A. M. (2012). Permainan Teka-Teki Silang Sebagai Media Dalam Pembelajaran Bahasa Arab (Mufradat). Jurnal Pemikiran Islam, 37(1), 15–26.
Mufidah, I., Nulhakim, L., & ... (2020). Development of learning media for video audio-visual stop motion based on contextual teaching and learning in science learning water cycle material. Jurnal Ilmiah Sekolah …. https://ejournal.undiksha.ac.id/index.php/JISD/article/view/27357
Mufidah, N., & Rohima, I. I. (2020). Pengajaran Kosa Kata Untuk Mahasiswa Kelas Intensif Bahasa Arab: Vocabulary Teaching for Arabic Intensive Class. Uniqbu Journal of Social Sciences. http://ejournal-uniqbu.ac.id/index.php/ujss/article/view/7
Mustafa, M. (2021). A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers. Journal of Ambient Intelligence and Humanized Computing, 12(3), 4101–4115. https://doi.org/10.1007/s12652-020-01790-w
Nayel, H. (2019). NAYEL@APDA: Machine learning approach for author profiling and deception detection in Arabic texts. In CEUR Workshop Proceedings (Vol. 2517, pp. 92–99). https://api.elsevier.com/content/abstract/scopus_id/85076892312
Omari, O. (2019). Variation in the acoustic correlates of emphasis in Jordanian Arabic: Gender and social class. Folia Linguistica, 53(1), 169–200. https://doi.org/10.1515/flin-2019-2007
Ramadhina, F. A. (2019). Online learning design for fundamental arabic language with user-centered design approach. In 2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019 (pp. 451–458). https://doi.org/10.1109/ICACSIS47736.2019.8979912
Romeo, S. (2019). Language processing and learning models for community question answering in Arabic. Information Processing and Management, 56(2), 274–290. https://doi.org/10.1016/j.ipm.2017.07.003
Sayed, A. A. (2020). Sentiment Analysis for Arabic Reviews using Machine Learning Classification Algorithms. In Proceedings of 2020 International Conference on Innovative Trends in Communication and Computer Engineering, ITCE 2020 (pp. 56–63). https://doi.org/10.1109/ITCE48509.2020.9047822
Sundus, K. (2019). A Deep learning approach for Arabic text classification. In 2019 2nd International Conference on New Trends in Computing Sciences, ICTCS 2019 - Proceedings. https://doi.org/10.1109/ICTCS.2019.8923083
Syafitri, W., & Artika, F. S. (2019). English as lingua franca and asean economic community (AEC): a perspective of islamic economy faculty in building strong muslim students in iain Bukittinggi. Islam Realitas: Journal of Islamic …. http://ejournal.iainbukittinggi.ac.id/index.php/Islam_realitas/article/view/1096
Zhang, J., Yu, Y., Tang, S., Wu, J., & Li, W. (2021). Variational Autoencoder with CCA for Audio-Visual Cross-Modal Retrieval. ArXiv Preprint ArXiv:2112.02601. https://arxiv.org/abs/2112.02601
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