ANALYSIS OF NOUN DERIVATION IN THE LAMPUNG LANGUAGE USING COMPUTATIONAL LINGUISTICS
DOI:
https://doi.org/10.21009/ishel.v2i1.69582Keywords:
affixation, computational linguistics, Lampung language., morphology, noun derivationAbstract
Lampung, one of Indonesia’s regional languages, faces significant challenges, including a declining number of speakers, limited digital linguistic documentation, and the lack of computational morphology studies capable of systematically mapping word-formation processes. As a result, nominal derivation patterns in Lampung, particularly across its two major dialects, remain insufficiently documented and analyzed. This study aims to examine nominal derivation processes in the Lampung language as a response to the urgent need for language preservation and documentation in the era of globalization. The research focuses on the two principal dialects, Dialect A (Api) and Dialect O (Nyo), by investigating affixation patterns, semantic categories, and morphological variations between the dialects. Data were collected from a representative Lampung language corpus and analyzed using computational linguistic approaches, including pattern extraction, frequency analysis, and the automatic mapping of morphological relationships. The findings reveal that the affixes pe-, N-, ke-an, and se-an are the most productive nominal derivational markers, each exhibiting specific semantic tendencies such as agent, instrument, resultative, and abstract concepts. Comparative analysis further demonstrates differences in affix distribution and form–meaning relationships between the two dialects. The main contribution of this study lies in providing a systematic and objective mapping of Lampung nominal derivational morphology, enriching linguistic documentation and establishing a foundation for the development of technology-based language resources to support the preservation and revitalization of regional languages.
References
Ahlberg, M., Forsberg, M., & Hulden, M. (2015). Paradigm classification in supervised learning of morphology. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1024–1029. https://doi.org/10.3115/v1/N15-1107
Amrulloh, M. A., & Humairo, L. (2020). Morpho-phonological of loanwords in Lampung language from Arabic. Arabiyat: Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban, 7(1), 78–97. https://doi.org/10.15408/a.v7i1.13174
Astawan, … & Sadwika, … (2020). (Complete this reference — cited in the Introduction but missing from the original list.)
Aysina, M. (n.d.). Word formation and structural features of the word. EPRA International Journal of Multidisciplinary Research (IJMR). Retrieved November 26, 2025, from https://eprajournals.com/IJMR/article/14844/abstract
B, P., K. P., S., & Kumar, M. A. (2018). A deep learning approach for Malayalam morphological analysis at character level. Procedia Computer Science, 132, 47–54. https://doi.org/10.1016/j.procs.2018.05.058
Budiono, S., Novita, R., & Syarfina, T. (2023). Mentawai language variations in the Mentawai Islands Regency, West Sumatra Province. Jurnal Arbitrer, 10(1), 8–18. https://doi.org/10.25077/ar.10.1.8-18.2023
Bunau, E. (2022). Morphology of the languages: Overview of the exemplifications of English, Indonesian, Iban, and Kanayatn. Journal of English Educational Study (JEES), 5(1), 77–85. https://doi.org/10.31932/jees.v5i1.1624
Chouhan, A., Mandour, S., & Gertz, M. (2025). ClusterChat: Multi-feature search for corpus exploration. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 4081–4085. https://doi.org/10.1145/3726302.3730137
Gomashie, G. A., & Terborg, R. (2021). Nahuatl, selected vitality indicators and scales of vitality in an Indigenous language community in Mexico. Open Linguistics, 7(1), 166–180. https://doi.org/10.1515/opli-2021-0010
Hameed, A. (2022). Mother, mother tongue, and language endangerment process: An exploratory study. Theory and Practice in Language Studies, 12(4), 726–735. https://doi.org/10.17507/tpls.1204.13
Hassan Idris Brama, A. (2023). Analyzing the impact of sample size and population distribution on statistical test power. International Journal of Science and Research (IJSR), 12(8), 2383–2390. https://doi.org/10.21275/SR23821121418
Jain, D. K., Eyre, Y. G.-M., Kumar, A., Gupta, B. B., & Kotecha, K. (2024). Knowledge-based data processing for multilingual natural language analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(5), 1–16. https://doi.org/10.1145/3583686
Kieffer, M. J., Biancarosa, G., & Mancilla-Martinez, J. (2013). Roles of morphological awareness in the reading comprehension of Spanish-speaking language minority learners: Exploring partial mediation by vocabulary and reading fluency. Applied Psycholinguistics, 34(4), 697–725. https://doi.org/10.1017/S0142716411000920
Kopotev, M. V. (2021). Some thoughts on corpus and general linguistics. Philological Class, 26(2), 90–102. https://doi.org/10.51762/1FK-2021-26-02-07
Lango, M., Žabokrtský, Z., & Ševčíková, M. (2021). Semi-automatic construction of word-formation networks. Language Resources and Evaluation, 55(1), 3–32. https://doi.org/10.1007/s10579-019-09484-2
Low, D. S., McNeill, I., & Day, M. J. (2022). Endangered languages: A sociocognitive approach to language death, identity loss, and preservation in the age of artificial intelligence. Sustainable Multilingualism, 21(1), 1–25. https://doi.org/10.2478/sm-2022-0011
Majumder, E. (2025). CulturalEcho: Multimodal deep learning approaches to language preservation, documentation, and community-led revitalization. International Journal of Scientific Research in Engineering and Management, 9(3), 1–9. https://doi.org/10.55041/IJSREM43064
Mazhitaeva, S. M., Vesselinov, D., & Amanbekova, D. Y. (2020). Language representation and the structure of concepts in the modern cultural linguistics. Bulletin of the Karaganda University. Philology Series, 99(3), 58–64. https://doi.org/10.31489/2020ph3/58-64
McIntosh, E. J., Chapman, S., Kearney, S. G., Williams, B., Althor, G., Thorn, J. P. R., Pressey, R. L., McKinnon, M. C., & Grenyer, R. (2018). Absence of evidence for the conservation outcomes of systematic conservation planning around the globe: A systematic map. Environmental Evidence, 7(1), 22. https://doi.org/10.1186/s13750-018-0134-2
Pollach, I. (2012). Taming textual data: The contribution of corpus linguistics to computer-aided text analysis. Organizational Research Methods, 15(2), 263–287. https://doi.org/10.1177/1094428111417451
Ponti, E. M., O'Horan, H., Berzak, Y., Vulić, I., Reichart, R., Poibeau, T., Shutova, E., & Korhonen, A. (2019). Modeling language variation and universals: A survey on typological linguistics for natural language processing. Computational Linguistics, 45(3), 559–601. https://doi.org/10.1162/coli_a_00357
Rahima, A. (2024). Language documentation as a revitalization effort: Regional languages are starting to extinct. International Journal of Multidisciplinary Sciences and Arts, 3(3), 72–76. https://doi.org/10.47709/ijmdsa.v3i3.4123
Seifart, F., Evans, N., Hammarström, H., & Levinson, S. C. (2018). Language documentation twenty-five years on. Language, 94(4), e324–e345. https://doi.org/10.1353/lan.2018.0070
Septianingtias, V., Wahya, Nur, T., & Ariyani, F. (2024). Lexical variation in the Lampung language, Indonesia. Cogent Arts & Humanities, 11(1), 2309740. https://doi.org/10.1080/23311983.2024.2309740
Septianingtias, V., et al. (2025). (Complete this reference — cited in the Introduction but missing from the original list.)
Skirgård, H., Haynie, H. J., Blasi, D. E., Hammarström, H., Collins, J., Latarche, J. J., Lesage, J., Weber, T., Witzlack-Makarevich, A., Passmore, S., Chira, A., Maurits, L., Dinnage, R., Dunn, M., Reesink, G., Singer, R., Bowern, C., Epps, P., Hill, J., … Gray, R. D. (2023). Grambank reveals the importance of genealogical constraints on linguistic diversity and highlights the impact of language loss. Science Advances, 9(16), eadg6175. https://doi.org/10.1126/sciadv.adg6175
Solan, Z., Horn, D., Ruppin, E., & Edelman, S. (2005). Unsupervised learning of natural languages. Proceedings of the National Academy of Sciences, 102(33), 11629–11634. https://doi.org/10.1073/pnas.0409746102
Szabo, G., & Kovacs, L. (2020). Automated learning of Hungarian morphology for inflection generation and morphological analysis. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 8(4), 746–756. https://doi.org/10.11591/ijeei.v8i4.2545
Tariq, T. R., Rana, M. A., Sultan, B., Asif, M., Rafique, N., & Aleem, S. (2020). An analysis of derivational and inflectional morphemes. International Journal of Linguistics, 12(1), 83. https://doi.org/10.5296/ijl.v12i1.16084
Tsabita, N. D., Amirullah, M. A., Muhdlar, H. Z. A., & Suaedi, H. (2025). Analisis penggunaan afiksasi bahasa Madura dalam buku paket Sekkar Anom 3 Bab 1 kelas IX SMP/MTs. BLAZE: Jurnal Bahasa dan Sastra dalam Pendidikan Linguistik dan Pengembangan, 3(1), 171–184. https://doi.org/10.59841/blaze.v3i1.2274
Wulandari, A., Mulyana, D., Hadisiwi, P., & Rizal, E. (2025). Negotiating identity through language: The meaning of Javanese speech levels among teenagers in Yogyakarta, Indonesia. International Journal of Innovative Research and Scientific Studies, 8(2), 2022–2032. https://doi.org/10.53894/ijirss.v8i2.5593
Zariquiey, R., Oncevay, A., & Vera, J. (2022). CLD² language documentation meets natural language processing for revitalising endangered languages. Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages, 20–30. https://doi.org/10.18653/v1/2022.computel-1.4
Zheng, C., Zhao, W., Yang, Z., Guo, S., & the Alzheimer's Disease Neuroimaging Initiative. (2025). Dysfunction in the hierarchy of morphometric similarity network in Alzheimer's disease and its correlation with cognitive performance and gene expression profiles. Psychological Medicine, 55, e42. https://doi.org/10.1017/S0033291725000091
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