Jul 2026· Jurnal Teknologi Informatika dan Komputer· Vol 12, pp. 826-834· 0 citations
TL;DR
This study develops a text detection system specifically for the Indonesian language using a Deep Learning approach with a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture that achieved a 93.77% success rate in identifying human-written texts, though it logged a lower detection rate for academic AI text structures.
Abstract
The rapid advancement of generative Artificial Intelligence (AI) presents challenges to academic integrity due to potential misuse like plagiarism. This study develops a text detection system specifically for the Indonesian language using a Deep Learning approach with a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. The research methodology follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A dataset comprising 5,008 text rows was compiled via web scraping from journalism platforms and academic journals indexed in SINTA 4 for human-written texts, while AI-generated counterparts were engineered using ChatGPT and Google Gemini paraphrases. Text features were extracted using a Keras Tokenizer and Embedding Layer with 64 dimensions. Evaluation of the trained Bi-LSTM model on a 30% validation split demonstrated an overall accuracy of 78.24% and a Mean Absolute Error (MAE) of 0.3295. Specifically, the model achieved a 93.77% success rate in identifying human-written texts, though it logged a lower detection rate of 62.62% for academic AI text structures. The final model was successfully deployed as a web application using Streamlit.
The aim of the research. The rapid advancement of generative artificial intelligence language models has introduced new complexities in discerning the authorship and quality of textual content. In this paper, we explored the feasibility of using perplexity – a measure of token predictability – as the only discriminative feature for classifying AI-generated versus human-written texts in Ukrainian within the IT domain. Our approach employed small language models to calculate perplexity and detect content generated by state-of-the-art models, evaluating the potential for lightweight solutions. Research results. Initial experiments using a single perplexity threshold across Gemma 3 / Llama 3.2 1B models yielded classification accuracies around 0.70. The full token-level probability sequences were proposed as feature vectors, enabling us to achieve an accuracy of 0.68 via simple KNN classification. Finally, the convolutional neural network architectures trained on these features allowed us to obtain 0.82–0.87 accuracy. Conclusions. The comparative analysis with a traditional NLP-based discriminative neural network model revealed that direct text piece classification outperforms perplexity-based methods, although the latter still demonstrate practical utility.
O. Gorokhovatskyi· Advanced Information Systems· 0 citations
It can be concluded that the Bi-LSTM model is effective for automatic text classification of educational questions and has strong potential for further development in technology-based question grouping systems.
This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language, demonstrating that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages.
Muhammad Rizky, Anandita Priatama, Aviv Yuniar Rahman et al.· Buana Information Technology...· 0 citations
A hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN) that enhances detection by modeling structural relationships within text data is proposed.
The comparative analysis with a traditional NLP-based discriminative neural network model revealed that direct text piece classification outperforms perplexity-based methods, although the latter still demonstrate practical utility.
Test set results show that Decoding-Enhanced Bert with Disentangled Attention (DeBERTa) achieves the highest macro F1 − Score of 85.48%, surpassing the previously top-ranked Multi-Task Learning (MTL) system, which attains a macro F1 of 83.07%.
Batyr Sharimbayev, S. Kadyrov· Journal of Advances in Infor...· 0 citations