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Misinformation has become an innocuous yet potentially harmful problem ever since the development of internet. Numbers of efforts are done to prevent the consumption of misinformation, including the use of artificial intelligence (AI), mainly natural language processing (NLP). Unfortunately, most of natural language processing use English as its linguistic approach since English is a high resource language. On the contrary, Indonesia language is considered a low resource language thus the amount of effort to diminish consumption of misinformation is low compared to English-based natural language processing. This experiment is intended to compare fastText and GloVe embeddings for four deep neural networks (DNN) models: long short-term memory (LSTM), bidirectional long short-term memory (BI-LSTM), gated recurrent unit (GRU) and bidirectional gated recurrent unit (BI-GRU) in terms of metrics score when classifying news between three classes: fake, valid, and satire. The latter results show that fastText embedding is better than GloVe embedding in supervised text classification, along with BI-GRU + fastText yielding the best result.

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