Implementasi Pembelajaran Bahasa Arab Berbasis Kecerdasan Buatan dalam Konteks Smart Learning

Muhammad Yusuf Salam, Nur Azlin Putri, Adam Mudinillah, Kartika Rahmadhani

Abstract


Konsep pembelajaran cerdas atau biasa kita ketahui dengan smart learning muncul sebagai
pendekatan inovatif untuk meningkatkan pembelajaran melalui teknologi informasi terkini. Smart
learning memungkinkan personalisasi, pemantauan ketat, dan akses ke sumber daya yang lebih
serbaguna dalam pengajaran dan pembelajaran. Dalam smart learning ini sangat banyak media
yang bisa kita gunakan, apalagi dalam perkembangan teknologi sekarang, di antara teknologi yang
digunakan dalam smart learning adalah kecerdasan buatan (AI) atau biasa di kenal oleh anak anak
zaman sekarang adalah Artificial Intelligence, teknologi ini telah menjadi kunci untuk memahami
dan mengatasi tantangan baru dalam pendidikan digital. Dalam penelitian ini, peneliti
menggunakan metode Survei dan Kuesioner, alat yang di gunakan untuk mengumpulkan data yang
dibutuhkan adalah menggunakan goggle from. Hasil dari penelitian ini adalah pembelajaran
menggunakan kecerdasan buatan dalam konteks smart learning sangat menguntungkan pelajar
maupun guru, karena semua yang dibutuhkan bisa disediakan dalam kecerdasan buatan (AI)
tersebut. Peneliti menyimpulkan bahwa implementasi pembelajaran berbasis AI adalah langkah
penting menuju Smart Learning yang lebih adaptif dan efisien, tetapi juga menekankan perlunya
kebijakan yang tepat untuk melindungi data siswa dan memastikan keberlanjutan inovasi dalam
pendidikan.


Full Text:

PDF

References


Daftar Rujukan

Alahmari, N., Alswedani, S., Alzahrani, A., Katib, I., Albeshri, A., & Mehmood, R. (2022). Musawah: A Data-

Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in

Saudi Arabia. Sustainability, 14(6), 3313. https://doi.org/10.3390/su14063313

Al-Ayyoub, M., Khamaiseh, A. A., Jararweh, Y., & Al-Kabi, M. N. (2019). A comprehensive survey of arabic

sentiment analysis. Information Processing & Management, 56(2), 320–342.

https://doi.org/10.1016/j.ipm.2018.07.006

Alizadeh, R., Abad, J. M. N., Ameri, A., Mohebbi, M. R., Mehdizadeh, A., Zhao, D., & Karimi, N. (2021). A machine

learning approach to the prediction of transport and thermodynamic processes in multiphysics

systems—Heat transfer in a hybrid nanofluid flow in porous media. Journal of the Taiwan Institute of

Chemical Engineers, 124, 290–306. https://doi.org/10.1016/j.jtice.2021.03.043

Aljarah, I., Habib, M., Hijazi, N., Faris, H., Qaddoura, R., Hammo, B., Abushariah, M., & Alfawareh, M. (2021).

Intelligent detection of hate speech in Arabic social network: A machine learning approach. Journal of

Information Science, 47(4), 483–501. https://doi.org/10.1177/0165551520917651

Al-Rahmi, W. M., Yahaya, N., Alturki, U., Alrobai, A., Aldraiweesh, A. A., Omar Alsayed, A., & Kamin, Y. B.

(2022). Social media – based collaborative learning: The effect on learning success with the

moderating role of cyberstalking and cyberbullying. Interactive Learning Environments, 30(8), 1434–

https://doi.org/10.1080/10494820.2020.1728342

Alsafari, S., Sadaoui, S., & Mouhoub, M. (2020). Hate and offensive speech detection on Arabic social media.

Online Social Networks and Media, 19, 100096. https://doi.org/10.1016/j.osnem.2020.100096

Beseiso, M., & Elmousalami, H. (2020). Subword Attentive Model for Arabic Sentiment Analysis: A Deep

Learning Approach. ACM Transactions on Asian and Low-Resource Language Information Processing,

(2), 1–17. https://doi.org/10.1145/3360016

Chang, S.-C., Hsu, T.-C., & Jong, M. S.-Y. (2020). Integration of the peer assessment approach with a virtual

reality design system for learning earth science. Computers & Education, 146, 103758.

https://doi.org/10.1016/j.compedu.2019.103758

da Costa, K. A. P., Papa, J. P., Lisboa, C. O., Munoz, R., & de Albuquerque, V. H. C. (2019). Internet of Things: A

survey on machine learning-based intrusion detection approaches. Computer Networks, 151, 147–157.

https://doi.org/10.1016/j.comnet.2019.01.023

Dahou, A., Elaziz, M. A., Zhou, J., & Xiong, S. (2019). Arabic Sentiment Classification Using Convolutional

Neural Network and Differential Evolution Algorithm. Computational Intelligence and Neuroscience,

, 1–16. https://doi.org/10.1155/2019/2537689

Davis, S. J., Lewis, N. S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I. L., Benson, S. M., Bradley, T., Brouwer, J.,

Chiang, Y.-M., Clack, C. T. M., Cohen, A., Doig, S., Edmonds, J., Fennell, P., Field, C. B., Hannegan, B.,

Hodge, B.-M., Hoffert, M. I., … Caldeira, K. (2018). Net-zero emissions energy systems. Science,

(6396), eaas9793. https://doi.org/10.1126/science.aas9793

Ding, D., Han, Q.-L., Xiang, Y., Ge, X., & Zhang, X.-M. (2018). A survey on security control and attack detection

for industrial cyber-physical systems. Neurocomputing, 275, 1674–1683.

https://doi.org/10.1016/j.neucom.2017.10.009

Elnagar, A., Al-Debsi, R., & Einea, O. (2020). Arabic text classification using deep learning models. Information

Processing & Management, 57(1), 102121. https://doi.org/10.1016/j.ipm.2019.102121

Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open

Research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358

Katoh, K., Rozewicki, J., & Yamada, K. D. (2019). MAFFT online service: Multiple sequence alignment,

interactive sequence choice and visualization. Briefings in Bioinformatics, 20(4), 1160–1166.

https://doi.org/10.1093/bib/bbx108

Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2019). Short-Term Residential Load Forecasting

Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, 10(1), 841–851.

https://doi.org/10.1109/TSG.2017.2753802

Li, M., Lu, J., Chen, Z., & Amine, K. (2018). 30 Years of Lithium‐Ion Batteries. Advanced Materials, 30(33),

https://doi.org/10.1002/adma.201800561

Maidiana, M. (2021). Penelitian Survey. ALACRITY : Journal of Education, 20–29.

https://doi.org/10.52121/alacrity.v1i2.23

Muchlis, A. F. (2023). Metode Penelitian Survei-Kuesioner untuk Kesesakan dan Privasi pada Hunian Asrama.

Jurnal Lingkungan Binaan Indonesia, 12(3), 154–163. https://doi.org/10.32315/jlbi.v12i3.252

Oueslati, O., Cambria, E., HajHmida, M. B., & Ounelli, H. (2020). A review of sentiment analysis research in

Arabic language. Future Generation Computer Systems, 112, 408–430.

https://doi.org/10.1016/j.future.2020.05.034

Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of

Intelligent Manufacturing, 31(1), 127–182. https://doi.org/10.1007/s10845-018-1433-8

Ritonga, A. W., Ritonga, M., Nurdianto, T., Kustati, M., Rehani, R., Lahmi, A., Yasmadi, Y., & Pahri, P. (2020). ELearning

Process of Maharah Qira’ah in Higher Education during the Covid-19 Pandemic.

International Journal of Higher Education, 9(6), 227. https://doi.org/10.5430/ijhe.v9n6p227

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN

Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation—A

review. Information Processing in Agriculture, 7(1), 1–19. https://doi.org/10.1016/j.inpa.2019.09.006

Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of Edge Computing and

Deep Learning: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 22(2), 869–904.

https://doi.org/10.1109/COMST.2020.2970550

Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019). Review of Smart Meter Data Analytics: Applications,

Methodologies, and Challenges. IEEE Transactions on Smart Grid, 10(3), 3125–3148.

https://doi.org/10.1109/TSG.2018.2818167

Wen, L., Gao, L., & Li, X. (2019). A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault

Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 136–144.

https://doi.org/10.1109/TSMC.2017.2754287

Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2019). Big Data Analytics in Intelligent Transportation

Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383–398.

https://doi.org/10.1109/TITS.2018.2815678


Refbacks

  • There are currently no refbacks.


This proceeding series is indexed by:

ipiii.pnggoogle.png

ISSN: 2987-2448