Deteksi Berita Hoaks Berbahasa Indonesia Menggunakan One-Dimensional Convolutional Neural Network - Muhammad Zuama Al Amin; Muhammad Ariful Furqon; Dwi Wijonarko (Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada, 2025)
The rapid advancement of information technology has enabled global information dissemination and led to a surge in hoax news, particularly in Indonesia. Hoax news poses a significant risk of spreading disinformation, potentially influencing public opinion, social stability, and security. Therefore, an effective technology-based solution is required to detect and identify hoaxes. This study aims to develop and optimize a one-dimensional convolutional neural network (1D-CNN) model to detect hoax news with high accuracy. The dataset comprised 12,151 articles, including 5,276 valid news items and 6,875 hoax news items, collected from reliable sources and anti-hoax platforms. The text preprocessing stages included data cleaning, case folding, punctuation removal, number removal, and stopword removal. The textual data were processed through tokenization and padding stages for model training preparation. The proposed 1D-CNN architecture integrated embedding, Conv1D, batch normalization, globalmaxpooling1d, dense, and dropout layers to enhance generalization capabilities and reduce the risk of overfitting. The model was trained using the Adam optimizer and its performance was evaluated using 10-fold cross-validation. Experimental results showed that the model achieved an average accuracy, precision, recall, and F1 score of 97.74%, 97.75%, 97.74%, and 97.73%, respectively. The developed model outperformed previous methods, namely the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM), gated recurrent unit (GRU), and conventional methods such as naïve Bayes or support vector machine (SVM), in terms of accuracy and training efficiency. This study demonstrates that the model has a reliable capability in identifying hoax news, both in terms of detection accuracy and performance consistency.