Universitas Padjadjaran, Indonesia
* Corresponding author
Universitas Padjadjaran, Indonesia
Universitas Padjadjaran, Indonesia
Universitas Padjadjaran, Indonesia

Article Main Content

The study was motivated by the factual condition of methodological and theoretical deficiencies promoting the mapping and classification studies of Sudanese Dialect.  The study aims to investigate (1) the comprehensive regional classification of Banjar Sundanese Dialect and (2) the linguistic and non-linguistic factor identification supporting the regional distance in the classification of Banjar Sundanese Dialect. In this case, the study applied a combination method (mixed research methods). The data were collected through participant observation. Furthermore, the calculation of Banjar Sundanese Dialect linguistic distance employed the Levenshtein algorithm in Gabmap. Multidimensional scaling was used to ensure the reliability of the clustering results. Based on the calculation results of the linguistic distance, Banjar Sundanese Dialect can be classified into three sub-dialects, namely the standard Sundanese sub-dialect; the Java-influenced sub-dialect; and the Java-dominated sub-dialect. The study reveals that there are significant differences between Banjar Sundanese Dialect, especially Purwaharja and Langensari sub-dialects, and Standard Sundanese Dialect. One of these differences is caused by the influence of the Javanese language.

References

  1. Daniels, D., Barth, D., & Barth, W. (2019). Subgrouping the Sogeram Languages A Critical Appraisal of Historical Glottometry. Journal of Historical Linguistics, 9(1), 92–127. https://doi.org/10.1075/jhl.17011.dan.
     Google Scholar
  2. Davitishvili, N. (2017). Cross-Cultural Awareness and Teaching English as a Second Language in the Context of Globalization. Sino-US English Teaching, 14(9), 549–558. https://doi.org/10.17265/1539-8072/2017.09.003.
     Google Scholar
  3. De Stefani, E., & De Marco, D. (2019). Language, Gesture, and Emotional Communication: An Embodied View of Social Interaction. Frontiers in Psychology, 10, 2063. https://doi.org/10.3389/fpsyg.2019.02063.
     Google Scholar
  4. DiStefano, C., Shih, W., Kaiser, A., Landa, R., & Kasari, C. (2016). Communication Growth in Minimally Verbal Children with ASD: The Importance of Interaction. Autism Research, 9(10), 1093–1102. https://doi.org/10.1002/aur.1594.
     Google Scholar
  5. Dorta, J., & González Rodríguez, M. J. (2019). Tonal Proximity Relationship in the Spanish of the Canary Islands in the Light of Dialectometry. In Languages (Vol. 4, Issue 2). https://doi.org/10.3390/languages4020029.
     Google Scholar
  6. Dunn, J. (2018). Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs. Cognitive Linguistics, 29(2), 275–311. https://doi.org/doi:10.1515/cog-2017-0029.
     Google Scholar
  7. Elias, A. (2019). Visualizing the Boni dialectswith Historical Glottometry. Journal of Historical Linguistics, 9(1), 70–91. https://doi.org/10.1075/jhl.18009.eli.
     Google Scholar
  8. Han, Y., & Wu, X. (2020). Language Policy, Linguistic Landscape and Residents’ Perception in Guangzhou, China: Dissents and Conflicts. Current Issues in Language Planning, 21(3), 229–253. https://doi.org/10.1080/14664208.2019.1582943.
     Google Scholar
  9. Jentoft, N., & Olsen, T. S. (2017). Against the Flow in Data Collection: How Data Triangulation Combined with a ‘Slow’ Interview Technique Enriches Data. Qualitative Social Work, 18(2), 179–193. https://doi.org/10.1177/1473325017712581.
     Google Scholar
  10. Kern, F. G. (2016). The Trials and Tribulations of Applied Triangulation: Weighing Different Data Sources. Journal of Mixed Methods Research, 12(2), 166–181. https://doi.org/10.1177/1558689816651032.
     Google Scholar
  11. Leddy-Cecere, T. A. (2021). Interrogating the Egypto-Sudanic Arabic Connection. In Languages (Vol. 6, Issue 3). https://doi.org/10.3390/languages6030123.
     Google Scholar
  12. Leinonen, T. (2016). Using Gabmap. Lingua, 178, 71–83. https://doi.org/10.1016/j.lingua.2015.02.004.
     Google Scholar
  13. Lopez-Dicastillo, O., & Belintxon, M. (2014). The Challenges of Participant Observations of Cultural Encounters within an Ethnographic Study. Procedia - Social and Behavioral Sciences, 132, 522–526. https://doi.org/https://doi.org/10.1016/j.sbspro.2014.04.347.
     Google Scholar
  14. McKim, C. A. (2015). The Value of Mixed Methods Research: A Mixed Methods Study. Journal of Mixed Methods Research, 11(2), 202–222. https://doi.org/10.1177/1558689815607096.
     Google Scholar
  15. Mondada, L. (2016). Challenges of Multimodality: Language and The Body in Social Interaction. Journal of Sociolinguistics, 20(3), 336–366. https://doi.org/10.1111/josl.1_12177.
     Google Scholar
  16. Munawarah, S., & Datang, F. A. (2019). Language Variations in Depok: A Study of Linguistic Lanscape and Dialectology. International Review of Humanities Studies, 4(2), 987–1001. https://doi.org/10.7454/irhs.v0i0.200.
     Google Scholar
  17. Nasrullah, R., Suganda, D., Wagiati, & Riyanto, S. (2019). Recovery Patterns and A Linguistic Therapy Model of Sundanese-Indonesian Bilingual Aphasia: A Neurolinguistic Study. Indonesian Journal of Applied Linguistics, 9(2), 452–462. https://doi.org/10.17509/ijal.v9i2.20243.
     Google Scholar
  18. Nerbonne, J. (2011). Gabmap - A web application for dialectology. Dialectologia, 65–89. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=81255136486&origin=inward.
     Google Scholar
  19. Pelto, P. J. (2015). What Is So New About Mixed Methods? Qualitative Health Research, 25(6), 734–745. https://doi.org/10.1177/1049732315573209.
     Google Scholar
  20. Prokić, J., & Nerbonne, J. (2008). Recognising Groups among Dialects. International Journal of Humanities and Arts Computing, 2(1–2), 153–172. https://doi.org/10.3366/e1753854809000366.
     Google Scholar
  21. Rahmawati, R., & Lestari, D. P. (2017). Java and Sunda dialect recognition from Indonesian speech using GMM and I-Vector. 2017 11th International Conference on Telecommunication Systems Services and Applications (TSSA), 1–5. https://doi.org/10.1109/TSSA.2017.8272892.
     Google Scholar
  22. Rahmi. (2015). The Development of Language Policy in Indonesia. Englisia Journal of Language, Education, and Humanities, 3(1), 9–22. https://doi.org/10.22373/ej.v3i1.622.
     Google Scholar
  23. Renz, S. M., Carrington, J. M., & Badger, T. A. (2018). Two Strategies for Qualitative Content Analysis: An Intramethod Approach to Triangulation. Qualitative Health Research, 28(5), 824–831. https://doi.org/10.1177/1049732317753586.
     Google Scholar
  24. Saddhono, K., & Hartanto, W. (2021). A Dialect Geography in Yogyakarta-Surakarta Isolect in Wedi District: An Examination of Permutation and Phonological Dialectometry as an Endeavor to Preserve Javanese Language in Indonesia. Heliyon, 7(7), e07660. https://doi.org/https://doi.org/10.1016/j.heliyon.2021.e07660.
     Google Scholar
  25. Simonÿ, C., Specht, K., Andersen, I. C., Johansen, K. K., Nielsen, C., & Agerskov, H. (2018). A Ricoeur-Inspired Approach to Interpret Participant Observations and Interviews. Global Qualitative Nursing Research, 5, 2333393618807395. https://doi.org/10.1177/2333393618807395.
     Google Scholar
  26. Sudaryanto, Soeparno, & Ferawati, L. (2019). Politics of Language in Indonesia (1975-2015): Study of History and Language Policy. Aksis: Jurnal Pendidikan Bahasa Dan Sastra Indonesia, 3(1), 129–139.
     Google Scholar
  27. Thamrin, H., & Isnendes, R. (2019). Sundanese Dialect in Sinar Resmi Traditional Village in Cisolok District, Sukabumi Regency (Phonological Perspective). Advances in Social Science, Education and Humanities Research, 430, 89–96.
     Google Scholar
  28. Tupas, R. (2015). Inequalities of Multilingualism: Challenges to Mther Tongue-Based Multilingual Education. Language and Education, 29(2), 112–124. https://doi.org/10.1080/09500782.2014.977295.
     Google Scholar
  29. Widyastuti, T. (2017). The Pangandaran Sundanese Dialect in Sidamulih District (Ponological Studies). Lokabasa, 8(1), 101–111. https://doi.org/10.17509/jlb.v8i1.15971.
     Google Scholar
  30. Wolk, C., & Szmrecsanyi, B. (2018). Probabilistic Corpus-Based Dialectometry. Journal of Linguistic Geography, 6(1), 56–75. https://doi.org/DOI: 10.1017/jlg.2018.6.
     Google Scholar