A Corpus-Based Analysis of Modal Auxiliaries of William Golding’s Novel “The Lord of Flies”
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This study aimed to identify the types of modal auxiliaries and the meanings of modal auxiliaries in the novel The Lord of Flies by William Golding. This study uses mixed methods. First, this study used corpus-based quantitative data to obtain modal auxiliary in The Lord of Flies novel examined by looking at the concordance and frequency of the Ant Conc software tools. Second, the data analysis process used qualitative methods to show the function or meaning of the modal auxiliary verbs in The Lord of Flies novel. Based on data analysis, it shows that there are 549 modal auxiliary verbs in The Lord of Flies novel divided into 9 types of modals, namely the modal could with an occurrence frequency of 161 times (24.49%), the modal can with an occurrence frequency of 136 times (24.91%), the modal would appear with a frequency of 111 times (20.33%), the modal might appear with a frequency of 41 times (7.69%), the modal must appear with a frequency of 37 times (6.78%), the modal should appear with a frequency of 22 times (4.03%), the modal will appear with a frequency of 15 times (2.75%), the modal may appear with a frequency of 14 times (2.56%), and the modal shall appear with a frequency of 8 times (1.47%). And the meanings of modal auxiliary verbs in The Lord of Flies novel are intentional, epistemic, deontic, and dynamic meanings.
Abdul Afwu Godly Prayitno, M. Z. M. (2020). PENGEMBANGAN KURIKULUM PEMBELAJARAN BAHASA ARAB PADA MASA PANDEMI COVID-19. AL-AF’IDAH: Jurnal Pendidikan Bahasa Arab dan Pengajarannya, 4(2), 15–29. https://doi.org/10.52266/al-afidah.v4i2.591
Al-Ayyoub, M., Nuseir, A., Alsmearat, K., Jararweh, Y., & Gupta, B. (2018). Deep learning for Arabic NLP: A survey. Journal of Computational Science, 26, 522–531. https://doi.org/10.1016/j.jocs.2017.11.011
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
Almarzooq, Z. I., Lopes, M., & Kochar, A. (2020). Virtual Learning During the COVID-19 Pandemic. Journal of the American College of Cardiology, 75(20), 2635–2638. https://doi.org/10.1016/j.jacc.2020.04.015
Alsubaie, M. A. (2022). Impacts of Technology in Learning: Mobile Typing Applications for Writing and Accomplishing Academic Tasks among Arabic-Speaking Undergraduate Students. Education Sciences, 12(12), 891. https://doi.org/10.3390/educsci12120891
Avisyah, G. F., Putra, I. J., & Hidayat, S. S. (2023). Open Artificial Intelligence Analysis using ChatGPT Integrated with Telegram Bot. Jurnal ELTIKOM, 7(1), 60–66. https://doi.org/10.31961/eltikom.v7i1.724
Balaha, H. M., Ali, H. A., Saraya, M., & Badawy, M. (2021). A new Arabic handwritten character recognition deep learning system (AHCR-DLS). Neural Computing and Applications, 33(11), 6325–6367. https://doi.org/10.1007/s00521-020-05397-2
Blanchete, I., Mourchid, M., Mbarki, S., & Mouloudi, A. (2018). Formalizing Arabic Inflectional and Derivational Verbs Based on Root and Pattern Approach Using NooJ Platform. Dalam S. Mbarki, M. Mourchid, & M. Silberztein (Ed.), Formalizing Natural Languages with NooJ and Its Natural Language Processing Applications (Vol. 811, hlm. 52–65). Springer International Publishing. https://doi.org/10.1007/978-3-319-73420-0_5
Boufenar, C., Kerboua, A., & Batouche, M. (2018). Investigation on deep learning for off-line handwritten Arabic character recognition. Cognitive Systems Research, 50, 180–195. https://doi.org/10.1016/j.cogsys.2017.11.002
Boukil, S., Biniz, M., Adnani, F. E., Cherrat, L., & Moutaouakkil, A. E. E. (2018). Arabic Text Classification Using Deep Learning Technics. International Journal of Grid and Distributed Computing, 11(9), 103–114. https://doi.org/10.14257/ijgdc.2018.11.9.09
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
Ertefaie, A., Small, D. S., & Rosenbaum, P. R. (2018). Quantitative Evaluation of the Trade-Off of Strengthened Instruments and Sample Size in Observational Studies. Journal of the American Statistical Association, 113(523), 1122–1134. https://doi.org/10.1080/01621459.2017.1305275
Guerrero-Ibáñez, J., Zeadally, S., & Contreras-Castillo, J. (2018). Sensor Technologies for Intelligent Transportation Systems. Sensors, 18(4), 1212. https://doi.org/10.3390/s18041212
Guillén-Gámez, F. D., & Mayorga-Fernández, M. J. (2020). Quantitative-comparative research on digital competence in students, graduates and professors of faculty education: An analysis with ANOVA. Education and Information Technologies, 25(5), 4157–4174. https://doi.org/10.1007/s10639-020-10160-0
Heikal, M., Torki, M., & El-Makky, N. (2018). Sentiment Analysis of Arabic Tweets using Deep Learning. Procedia Computer Science, 142, 114–122. https://doi.org/10.1016/j.procs.2018.10.466
Holly, S., Maulik, B., & Samuel, I. (2023). Use of Whatsapp as A Learning Media to Increase Students’ Learning Interest. Scientechno: Journal of Science and Technology, 2(1), 35–48. https://doi.org/10.55849/scientechno.v2i1.57
Huang, P., Miao, Q., Sang, G., Zhou, Y., & Jia, M. (2021). Research on quantitative method of particle segregation based on axial center nearest neighbor index. Minerals Engineering, 161, 106716. https://doi.org/10.1016/j.mineng.2020.106716
Jung, Y. M. (2019). Data Analysis in Quantitative Research. Dalam P. Liamputtong (Ed.), Handbook of Research Methods in Health Social Sciences (hlm. 955–969). Springer Singapore. https://doi.org/10.1007/978-981-10-5251-4_109
Kamorudeen, B. (2021). The Impact of Information and Communication Technology on the Teaching and Learning of Arabic in Nigeria. Journal of Muslim Minority Affairs, 41(4), 608–614. https://doi.org/10.1080/13602004.2022.2029011
Kato, N., Mao, B., Tang, F., Kawamoto, Y., & Liu, J. (2020). Ten Challenges in Advancing Machine Learning Technologies toward 6G. IEEE Wireless Communications, 27(3), 96–103. https://doi.org/10.1109/MWC.001.1900476
Larabi Marie-Sainte, S., Alalyani, N., Alotaibi, S., Ghouzali, S., & Abunadi, I. (2019). Arabic Natural Language Processing and Machine Learning-Based Systems. IEEE Access, 7, 7011–7020. https://doi.org/10.1109/ACCESS.2018.2890076
Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, 101162. https://doi.org/10.1016/j.learninstruc.2018.08.002
Lulu, L., & Elnagar, A. (2018). Automatic Arabic Dialect Classification Using Deep Learning Models. Procedia Computer Science, 142, 262–269. https://doi.org/10.1016/j.procs.2018.10.489
Mensah, C. N., Long, X., Dauda, L., Boamah, K. B., Salman, M., Appiah-Twum, F., & Tachie, A. K. (2019). Technological innovation and green growth in the Organization for Economic Cooperation and Development economies. Journal of Cleaner Production, 240, 118204. https://doi.org/10.1016/j.jclepro.2019.118204
Mohammed, A., & Kora, R. (2019). Deep learning approaches for Arabic sentiment analysis. Social Network Analysis and Mining, 9(1), 52. https://doi.org/10.1007/s13278-019-0596-4
Mulyasari, D., Noer, R. M., Sari, N., Ermawaty, E., Triharyadi, F., Tampubolon, D., & Catherine, S. (2023). Improving Health Status in The Elderly Through Health Checks and Education at Nuriah Nursing Homes in Karimun. Pengabdian: Jurnal Abdimas, 1(2), 75–81. https://doi.org/10.55849/abdimas.v1i2.183
Mustapa, A. M., Ghani, S. Ab., Rahman, M. A., Nawawi, Z., & Kamarudin, M. A. (2022). Evaluation of the online arabic treasure hunt as learning games activities using the technology acceptance model (TAM). 040017. https://doi.org/10.1063/5.0092716
Osamy, W., Salim, A., Khedr, A. M., & El-Sawy, A. A. (2021). IDCT: Intelligent Data Collection Technique for IoT-Enabled Heterogeneous Wireless Sensor Networks in Smart Environments. IEEE Sensors Journal, 21(18), 21099–21112. https://doi.org/10.1109/JSEN.2021.3100339
Pamuji, S., & Limei, S. (2023). The Managerial Competence Of The Madrasa Head In Improving Teacher Professionalism And Performance At Mi Al-Maarif Bojongsari, Cilacap District. Pengabdian: Jurnal Abdimas, 1(2), 66–74. https://doi.org/10.55849/abdimas.v1i2.158
Ramadhan, F. K., Faris, M. I., Wahyudi, I., & Sulaeman, M. K. (2023). PEMANFAATAN CHAT GPT DALAM DUNIA PENDIDIKAN. Jurnal Ilmiah Flash, 9(1), 25. https://doi.org/10.32511/flash.v9i1.1069
Shorman, S., Jarrah, M., & Alsayed, A. R. (2022). The Websites Technology for Arabic Language Learning Through COVID-19 Pandemic. Dalam A. Hamdan, A. Harraf, P. Arora, B. Alareeni, & R. Khamis Hamdan (Ed.), Future of Organizations and Work After the 4th Industrial Revolution (Vol. 1037, hlm. 327–340). Springer International Publishing. https://doi.org/10.1007/978-3-030-99000-8_18
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
Staszak, M., Staszak, K., Wieszczycka, K., Bajek, A., Roszkowski, K., & Tylkowski, B. (2022). Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIREs Computational Molecular Science, 12(2). https://doi.org/10.1002/wcms.1568
Tan, J., Liu, W., Xie, M., Song, H., Liu, A., Zhao, M., & Zhang, G. (2019). A low redundancy data collection scheme to maximize lifetime using matrix completion technique. EURASIP Journal on Wireless Communications and Networking, 2019(1), 5. https://doi.org/10.1186/s13638-018-1313-0
Thaher, T., Saheb, M., Turabieh, H., & Chantar, H. (2021). Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models. Symmetry, 13(4), 556. https://doi.org/10.3390/sym13040556
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
Tuttle, J. F., Blackburn, L. D., Andersson, K., & Powell, K. M. (2021). A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling. Applied Energy, 292, 116886. https://doi.org/10.1016/j.apenergy.2021.116886
Vicky, D., Adrianna, H., & Phan, B. (2023). Use of Gadgets by Early Childhood in the Digital Age to Increase Learning Interest. Scientechno: Journal of Science and Technology, 2(1), 17–34. https://doi.org/10.55849/scientechno.v2i1.58
Wang, B., Hu, S. J., Sun, L., & Freiheit, T. (2020). Intelligent welding system technologies: State-of-the-art review and perspectives. Journal of Manufacturing Systems, 56, 373–391. https://doi.org/10.1016/j.jmsy.2020.06.020
Weidlich, J., & Bastiaens, T. J. (2018). Technology Matters – The Impact of Transactional Distance on Satisfaction in Online Distance Learning. The International Review of Research in Open and Distributed Learning, 19(3). https://doi.org/10.19173/irrodl.v19i3.3417
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599
Zhang, J., Yu, J., Fu, S., & Tian, X. (2021). Adoption value of deep learning and serological indicators in the screening of atrophic gastritis based on artificial intelligence. The Journal of Supercomputing, 77(8), 8674–8693. https://doi.org/10.1007/s11227-021-03630-w
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