Native ads are a popular form of online advertisement that has a similar style and function to that of the original content of the platform they are displayed on. There are several problems in the sponsorship disclosure of native ads, namely their positioning, eminence, and lucidity of meaning, causing readers to not recognize them as advertisement. Unlike the selling message of traditional ads that are explicit, the selling message of native ads are implicit. This study aims to carry out the detection of native ads using deep ensemble-based models. The ensemble learning approach is adopted by combining two deep learning models using dense layers. Furthermore, to overcome the overfitting problem, an attention mechanism using a dense layer is implemented within the model. The experimental results show that the BiLSTM-CNN model with attention mechanism and parameter tuning was able to overcome the overfitting problem and achieve the highest accuracy of 95% for the detection of native ads.