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All Journal Journal of Information Technology
Yuliana Dewi Proboningrum
Universitas Duta Bangsa Surakarta
Computer Science & IT

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Implementasi NLP untuk Deteksi Teks Buatan AI (Chat-GPT) menggunakan Metode Naive Bayes Rafel Fernando; Yuliana Dewi Proboningrum; Septi Dwi Supriati; Nurmalitasari Nurmalitasari
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2026

Abstract

The development of artificial intelligence (AI) technology, especially large language models like ChatGPT, presents challenges related to the authenticity and validity of digital content. AI's ability to produce human-like text opens up opportunities for misuse, such as plagiarism and information manipulation. This study aims to develop an AI text detection system using the Multinomial Naive Bayes algorithm, due to its ease of use and high effectiveness algorithm has become a popular choice for text classification.. The dataset used is the Human ChatGPT Comparison Corpus (H3C), sourced from the ELI5 subreddit on Reddit, consisting of 800 entries of questions and answers from both humans and AI. The labeling process involves combining answers into a single column and assigning labels based on the source. Preprocessing steps include case folding, removal of digits and punctuation, tokenization, stopword removal, normalization, and text finalization. Text features are extracted using the TF-IDF method, limited to the top 1000 features. The model is trained on 80% of the data and tested on the remaining 20%. The evaluation shows an accuracy of 93%. These findings suggest that the Naive Bayes method is effective in distinguishing AI-generated from human-generated text and has potential as an automatic AI content detection tool.
Topic Modeling and Sentiment Analysis of YouTube Comments on OpenAI’s Stargate Project Using BERTopic and RoBERTa Yuliana Dewi Proboningrum; Hanifah Permatasari; Vihi Atina
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i03.2395

Abstract

The rapid development of generative artificial intelligence has sparked growing interest, widespread adoption across various sectors, and intense public debate, particularly following the announcement of the Stargate project by OpenAI, SoftBank, and Oracle. The diverse public reactions documented on social media platforms, particularly in YouTube comment sections, can serve as a valuable source of public opinion data. The YouTube comment data used in this study was collected through scraping and subsequently processed through a cleaning procedure, yielding clean data covering the period from January 21, 2025, to April 21, 2026. This study aims to identify dominant discussion topics and the underlying sentiment to uncover indications of public concerns and expectations regarding the AI development project led by OpenAI through the Stargate initiative. The analysis in this study employs a structured NLP approach, specifically BERTopic for topic modeling and RoBERTa for sentiment analysis. The RoBERTa model was fine tuned for binary classification using a combination of pseudo labeling, annotation, class balancing, and classification decision threshold optimization, which optimally yielded an accuracy of 0.89 and a macro F1 score of 0.87 in the two class evaluation. BERTopic successfully identified 43 topics with a coherence score of 0.58 and a topic diversity of 0.71.
Co-Authors Nurmalitasari Nurmalitasari Permatasari, Hanifah Rafel Fernando Septi Dwi Supriati Vihi Atina