Transformation of Indonesian Language in Social Media Using AI Expert Systems and Machine Learning
This study explores the transformation of the Indonesian language on social media platforms by integrating advanced artificial intelligence techniques, including expert systems and machine learning algorithms. The rapid evolution of communication on platforms such as TikTok, Twitter, and Instagram has led to a dynamic shift in language use, characterized by increased creativity in slang, abbreviations, and code-mixing, alongside challenges in maintaining compliance with standard language norms. By applying intelligent systems capable of analyzing large volumes of social media data, this research aims to identify patterns of linguistic innovation and deviations from conventional language rules. The expert system framework supports automated detection and classification of language variations, while machine learning models enhance the accuracy and adaptability of language analysis over time. Findings indicate that AI-driven approaches can effectively balance the dual needs of fostering linguistic creativity and preserving language standards, providing valuable insights for language educators, policymakers, and digital content developers. The study underscores the potential of intelligent technologies to facilitate language monitoring and support efforts in maintaining the cultural identity of the Indonesian language amid the fast-paced digital communication landscape.
Authors
- Nuke Puji Lestari Santoso (ORCID: https://orcid.org/0000-0002-4414-2102)
- Bhupesh Rawat (ORCID: https://orcid.org/0000-0001-7445-760X)
- Danang Danang
- Ruli Supriati
- Sanda Ramadhan Ratri
- Dwi Ferdiyatmoko Cahya Kumoro
- Elisa Ananda Natalia
Institutions
- Satya Wacana Christian University (ID)
- Universitas Pembangunan Panca Budi Medan (ID)
- Universitas Pembangunan Nasional Veteran Jakarta (ID)
- Graphic Era University (IN)
Publication Details
- Journal
- International Transactions on Artificial Intelligence (ITALIC)
- Published
- 2025-05-08
- DOI
- https://doi.org/10.33050/italic.v3i2.806
- Citations
- 1
- Primary Topic
- Edcuational Technology Systems
- Type
- article
- Field-Weighted Citation Impact
- 2.48