MEMBEDAKAN AUTENTISITAS GAGASAN PENULIS MANUSIA DAN TEKS GENERATIF AI BERBAHASA INDONESIA

Authors

  • Nurrahmayani STAI Al-Gazali Soppeng

DOI:

https://doi.org/10.23969/jp.v11i03.65123

Keywords:

Originality of Ideas, Generative Artificial Intelligence, Discourse Analysis, Academic Integrity, Indonesian-Language Texts.

Abstract

The rapid development of generative artificial intelligence (Generative AI) technology in the production of Indonesian-language text presents new challenges for maintaining academic integrity and the authenticity of written works. This phenomenon has sparked concerns about the erosion of originality of ideas and the increasingly blurred line between original human thought and algorithmic output. This study aims to formulate comprehensive differentiation indicators to distinguish the authenticity of human authors’ ideas from AI-generated texts based on the Indonesian language. The method used is qualitative descriptive analysis through critical discourse analysis and syntactic analysis of a comparative corpus, consisting of students’ academic papers and AI-generated texts based on similar (prompts). The evaluation focused on the depth of argumentation, cohesion-coherence structure, stylistic dynamics, and syntactic uniqueness. The results indicate that AI-generated texts tend to exhibit rigid sentence patterns, repetitive logical structures, and a lack of constructions reflecting subjective experiential context. In contrast, human writing exhibits rhetorical depth, flexibility in syntactic variation, and originality of ideas rooted in critical reasoning. These findings yield a new analytical framework that higher education institutions can apply to detect and validate the authenticity of academic works in Indonesian more objectively.

Downloads

Download data is not yet available.

References

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM FAccT Conference, 610–623. https://doi.org/10.1145/3442188.3445922

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., … Fiedel, N. (2023). PaLM: Scaling language modeling with Pathways. Journal of Machine Learning Research, 24(240), 1–113.

Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). “So what if the chatbot wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Fairclough, N. (1992). Discourse and social change. Cambridge, UK: Polity Press.

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023). Comparing scientific abstracts written by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. NPJ Digital Medicine, 6(1), 75. https://doi.org/10.1038/s41746-023-00819-6

Illingworth, S. (2023). Developing a personal voice in academic writing in the age of AI. Higher Education Pedagogies, 8(1), 1–12. https://doi.org/10.1080/23752696.2023.2241512

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On the opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Laufer, M., Leiser, A., Deacon, B., de Brún, A., & Bakhshi, S. (2024). Digital sovereignty and academic authenticity in higher education: Evaluating generative AI outputs in non-English contexts. Computers and Education: Artificial Intelligence, 6, 100201. https://doi.org/10.1016/j.caeai.2024.100201

OpenAI. (2023). GPT-4 technical report (ArXiv Preprint No. arXiv:2303.08774). Diperoleh dari https://arxiv.org/abs/2303.08774

Perkins, M. (2023). Academic integrity in the automated age: A generative AI risk assessment framework. International Journal for Educational Integrity, 19(1), 20. https://doi.org/10.1007/s40979-023-00144-0

Ricoeur, P. (1981). Hermeneutics and the human sciences: Essays on language, action and interpretation (J. B. Thompson, Ed. & Penerj.). Cambridge, UK: Cambridge University Press.

Sujarna, A., & Rahmawati, E. (2024). Analisis gaya bahasa dan struktur sintaksis wacana akademik mahasiswa vs teks generatif kecerdasan buatan. Jurnal Linguistik Terapan Indonesia, 12(1), 45–58. https://doi.org/10.21107/jlti.v12i1.18920

Downloads

Published

2026-08-19