Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informatika dan Komputer
Rafil Moehamad Alif
STT Wastukancana
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementation of the BiLSTM Model for Detecting AI-Generated Indonesian Text Rafil Moehamad Alif; Syariful Alam; Chandra Dewi Lestari
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3551

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.
Co-Authors Chandra Dewi Lestari Syariful Alam