Indonesia is the world’s fourth most populous country and has a diverse sociopolitical landscape. Political fake news exacerbates existing social divisions and causes political polarization in Indonesian society. Hence, studying it as a specific challenge can contribute to broader discussions on the impact of fake news in different contexts. The researchers propose a hoax news detection system by developing a deep learning model with various lapses against a data set preprocessed using term-frequency and token filtering to represent the most prominent words in each class. The researchers compare the layers with the potential to have high performance in predicting the falsity of Indonesian political news data by observing the models based on training history plots, model specification, and performance metrics in the classification report module. The deep learning models include One-Dimensional Convolution Neural Networks (1D CNN), Long-Term Short Memory (LSTM), and Gated Recurrent Unit (GRU). The news data are obtained from the Kaggle site, containing 41.726 rows of data. Based on the experiments with the text data that has been preprocessed in the form of vectors and the specific parameters before starting, the results show that GRU achieves the highest performance value in accuracy, recall, precision, and F1 score. Although GRU becomes the model with the smallest file size, it is the slowest model to generate predictions from text news data. It also has a higher potential to be an overfitted model due to parameters than a simple RNN.
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