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All Journal Al Ishlah Jurnal Pendidikan JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Charity : Jurnal Pengabdian Masyarakat IJoICT (International Journal on Information and Communication Technology) Proceeding of Community Service and Engagement
Prati Hutari Gani
Telkom University
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Text Classification Using Large Language Models to Detect AI and Human-Generated Sentences Arfan Rahman Yudiantoro; Prati Hutari Gani; Donni Richasdy
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.11223

Abstract

With the rapid development of the technology of Artificial Intelligence (AI), especially Large Language Models (LLMs) like ChatGPT, GPT-4 and Gemini, systems have become able to generate texts very similar to human writing. This similarity has been a boon to various sectors, but also poses fresh challenges of content authenticity and data integrity. A significant challenge is finding a way to automatically and accurately distinguish AI-generated sentences from human-written ones. This research focuses on building a text classification model that uses Large Language Models to distinguish between AI-generated and human-written sentences. This approach of research is based on recent research models that combine deep learning and classification using LLMs . The research process involves gathering human and AI-generated text data, pre-processing the text to normalize and tokenize it, feature extraction using the embeddings of large language models like IndoBERT, training the binary classification model, and assessing the model's performance using the metrics accuracy, precision, recall, and F1-score. Keywords: Text clustering, Large language models, AI text identification, Text authenticity.
Co-Authors Adinda Putri Rosyadi Andre Agasi Simanungkalit Arfan Rahman Yudiantoro Arfive Gandhi Arie Ardiyanti Suryani Azka Zainur Azifa Donni Richasdy Fhira Nhita Gusti Ayu Putri Saptawati Hasmawati Hasmawati Indra Lukmana Sardi Rianda Khusuma Rimbawan, Awan Rinda Firma Violita Sri Widowati Warih Maharani Yantrisnandra Akbar Maulino