With the rapid growth of social networks and technology, knowing what news to believe and what not to believe become a challenge in this digital era. Fake news is defined as provably erroneous information transmitted intending to defraud. This kind of misinformation poses a serious threat to social cohesion and well-being, since it fosters political polarisation and can destabilize trust in the government or the service provided. As a result, fake news detection has emerged as an important field of study, with the goal of identifying whether a certain piece of content is real or fake. In this paper, we propose a novel hybrid fake news detection system that combines a BERT-based (bidirectional encoder representations from transformers) with a light gradient boosting machine (LightGBM) model. We compare the performance of the proposed method to four different classification approaches using different word embedding techniques on three real-world fake news datasets to validate the performance of the proposed method compared to other methods. The proposed method is evaluated to detect fake news based on the headline-only or full text of the news content. The results show the superiority of the proposed method for fake news detection compared to many state-of-the-art methods.