Development of the Alhidayah Dictionary Application as a Digital Media for Arabic Language Research Terms
DOI:
https://doi.org/10.58223/al-irfan.v8i2.664Keywords:
Arabic Language, Research Terminology Dictionary, Dictionary DevelopmentAbstract
In practice, many individuals still make errors when translating certain Arabic terms. These mistakes frequently occur in the translation of research-related terms, which often consist of two-word constructions. Such terms are commonly translated partially or word by word, resulting in inaccurate or misleading interpretations. Therefore, this study aims to develop a dictionary application of Arabic research terminology, particularly terms related to educational research. This application is named Alhidayah Dictionary. This study employs a Research and Development (R&D) methodology. The development model adopted in this research is the ADDIE model, developed by Reiser and Mollenda, which consists of five stages: analysis, design, development, implementation, and evaluation. Data collection techniques used in this study include documentation and questionnaires. The questionnaires were utilized to assess product feasibility and to gather user responses. The collected data were analyzed using percentage-based statistical analysis. Based on the validation results from expert reviewers, which were subsequently converted using a media feasibility interpretation criteria table, the developed Alhidayah Dictionary application obtained a feasibility percentage of 79%. This result indicates that the application falls into the category of highly feasible. However, one limitation of this dictionary application is the requirement to pay an annual domain fee in order to maintain its accessibility. It is therefore expected that future researchers will be able to develop a more efficient and up-to-date dictionary application.
References
Abdelali, A., Mubarak, H., Samih, Y., Hassan, S., & Darwish, K. (2021). QADI: Arabic dialect identification in the wild. In Proceedings of the Sixth Arabic NLP Workshop (pp. 1–10).
Abu Farha, I., & Magdy, W. (2021). A comparative study of effective approaches for Arabic sentiment analysis. Information Processing & Management, 58(2), 102438. https://doi.org/10.1016/j.ipm.2020.102438
Ahmala, M. (2018). “Kamus Aplikasi” sebagai media pendamping Buku ‘al-arabiyah al-mu’āṣiroh.’ Alfazuna: Jurnal Pembelajaran Bahasa Arab dan Kebahasaaraban, 3(1), 32–50. https://doi.org/10.15642/alfazuna.v3i1.266
Albalawi, Y., Buckley, J., & Nikolov, N. S. (2021). Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media. Journal of Big Data, 8(1), 95. https://doi.org/10.1186/s40537-021-00488-w
Albtoush, E. S., Gan, K. H., & Alrababa, S. A. A. (2025). Fake news detection: State-of-the-art review and advances with attention to Arabic language aspects. PeerJ Computer Science, 11, e2693. https://doi.org/10.7717/peerj-cs.2693
Al-Moslmi, T., Albared, M., Al-Shabi, A., Omar, N., & Abdullah, S. (2017). Arabic senti-lexicon: Constructing publicly available language resources for Arabic sentiment analysis. Journal of Information Science, 44(3), 345–362. https://doi.org/10.1177/0165551516683908
Alqurashi, S., Alhindi, A., & Alanazi, E. (2020). Large Arabic Twitter dataset on COVID-19. arXiv Preprint. https://arxiv.org/abs/2004.04315
Aly, S., et al. (2020). DeepArSLR: A novel signer-independent deep learning framework for isolated Arabic sign language gestures recognition (Vol. 8). https://creativecommons.org/licenses/by/4.0/
Antoun, W., Baly, F., & Hajj, H. (2020). AraBERT: Transformer-based model for Arabic language understanding. In Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020) (pp. 1–7). Marseille, France.
Arifudin, A. (2020). Pengembangan Kamus Al-Af’āl dalam meningkatkan kemahiran menulis pada mahasiswa Prodi Pendidikan Bahasa Arab IAIN Pontianak. Lisanan Arabiya: Jurnal Pendidikan Bahasa Arab, 4(1), 57–77. https://doi.org/10.32699/liar.v4i1.1255
Bashir, M., Azmi, A., Nawaz, H., Zaghouani, W., Diab, M., Al-Fuqaha, A., & Qadir, J. (2021). Arabic natural language processing for Qur’anic research: A systematic review. Artificial Intelligence Review, 56, 6801–6854. https://doi.org/10.1007/s10462-022-10313-2
Branch, R. M., & Dousay, T. A. (2020). Survey of instructional design models. Association for Educational Communications and Technology.
Creswell, J. W., & Guetterman, T. C. (2021). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.
Elsabagh, A., Azab, S., & Hefny, H. (2025). A comprehensive survey on Arabic text augmentation: Approaches, challenges, and applications. Neural Computing and Applications, 37, 7015–7048. https://doi.org/10.1007/s00521-025-11020-z
Elsamadony, O., Keshk, A., & Abdelatey, A. (2021). Sentiment analysis for Arabic language using word embedding. In Proceedings of ICENCO 2021 (pp. 51–56).
Essa, N., El-Gayar, M. M., & El-Daydamony, E. M. (2025). Enhanced model for abstractive Arabic text summarization using natural language generation and named entity recognition. Neural Computing and Applications, 37, 7279–7301. https://doi.org/10.1007/s00521-024-10949-x
Fouad, M. M., Mahany, A., Aljohani, N., Abbasi, R. A., & Hassan, S.-U. (2020). ArWordVec: Efficient word embedding models for Arabic tweets. Soft Computing, 24(11), 8061–8068.
Gall, J. P., & Borg, W. R. (2020). Applying educational research: How to read, do, and use research to solve problems of practice. Pearson.
Habib, M., Faris, M. F., Alomari, A., & Faris, H. (2021). AltibbiVec: A word embedding model for medical and health applications in the Arabic language. IEEE Access, 9, 133875–133888.
Hamdy, A., et al. (2025). Arab2Vec: An Arabic word embedding model for use in Twitter NLP applications. PLOS One. https://doi.org/10.1371/journal.pone.0328369
Hamdy, A., Youssef, A., & Ryan, C. (2021). Arabic hands-on analysis, clustering and classification of a large Arabic Twitter dataset on COVID-19. International Journal of Simulation—Systems, Science & Technology, 22(1), 6–1.
Hanif, A., et al. (2023). Development of a digital dictionary for measuring Arabic language education students’ retention. Migration Letters, 20(5). https://www.migrationletters.com
Hidayat, F., & Nizar, M. (2021). Model ADDIE (Analysis, Design, Development, Implementation and Evaluation) dalam pembelajaran Pendidikan Agama Islam. Jurnal Inovasi Pendidikan Agama Islam (JIPAI), 1(1), 28–38. https://doi.org/10.15575/jipai.v1i1.11042
John Depsey, & Raiser, A. R. (t.t.). Trend and issue in instructional design and technology. Pearson Education.
Khalil, E. A., Hakim, E. M. F., & El Houby, H. K. (2021). Deep learning for emotion analysis in Arabic tweets. Journal of Big Data, 8(1), 1–15.
Mayer, R. E. (2020). Multimedia learning. Cambridge University Press.
Muaad, A., Heyat, M., Akhtar, F., Naseem, U., Naji, W., Mallappa, S., & J., H. (2025). Artificial intelligence for text analysis in the Arabic and related Middle Eastern languages: Progress, trends, and future recommendations. International Journal of Intelligent Systems, 2025. https://doi.org/10.1155/int/6091900
Munawarah, & Zulkiflih. (2021). Pembelajaran keterampilan menulis (Maharah al-Kitabah) dalam Bahasa Arab. Loghat Arabi: Jurnal Bahasa Arab dan Pendidikan Bahasa Arab, 1(2), 22. https://doi.org/10.36915/la.v1i2.15
Oueslati, O., Cambria, E., Hajhmida, M., & Ounelli, H. (2020). A review of sentiment analysis research in Arabic language. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2020.05.034
Pujiati, P., et al. (2025). Representing Arab-Indonesian identity: Language and cultural narratives on social media. Indonesian Journal of Applied Linguistics, 14(3), 653–666. https://doi.org/10.17509/ijal.v14i3.78286
Rahmawati, R. D., & Liana, I. (2021). Pengembangan kamus saku Arab-Indonesia untuk meningkatkan keterampilan membaca siswa kelas VIII di Pesantren Roudhotul Qur’an An-Noer. Dinamika: Jurnal Kajian Pendidikan dan Keislaman, 6(1), 41–54. https://doi.org/10.32764/dinamika.v6i1.1273
Rajab, S., Yusoff, N., & Aziz, M. (2025). Traditional or digital? Inspiring teachers’ preferences in Arabic language primary education in Malaysia. Human Behavior and Emerging Technologies. https://doi.org/10.1155/hbe2/1788597
Rina, D. R. (2021). Nal education and development. Jurnal Education and Development Institut Pendidikan Tapanuli Selatan, 9(3), 4.
Sugiyono. (2020). Metode penelitian dan pengembangan (Research and Development). Alfabeta.
Sunaryo, A., Patoni, A., & Basiroh, U. (1990). Pedoman penyusunan kamus dwibahasa. Departemen Pendidikan dan Kebudayaan.
Taherdoost, H. (2021). Data collection methods and tools for research. International Journal of Academic Research in Management.
Wazery, Y. M., et al. (2022). Abstractive Arabic text summarization based on deep learning. Computational Intelligence and Neuroscience, 2022, Article 1566890, 1–14. https://doi.org/10.1155/2022/1566890
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nawang Wulandari

This work is licensed under a Creative Commons Attribution 4.0 International License.
Lisensi :
Al-Irfan: Journal of Arabic Literature and Islamic Studies is published under conditions Creative Commons Attribution 4.0 International License / CC BY 4.0 This license permits anyone to copy and redistribute this material in any form or format, modify, modify, and make derivative works of this material for any purpose, including commercial purposes, so long as they credit the author for the original work.





