  Feature extraction is an important process of machine learning and deep
learning, as the process make algorithms function more efficiently, and also
accurate. In natural language processing used in deception detection such as
fake news detection, several ways of feature extraction in statistical aspect
had been introduced (e.g. N-gram). In this research, it will be shown that by
using deep learning algorithms and alphabet frequencies of the original text of
a news without any information about the sequence of the alphabet can actually
be used to classify fake news and trustworthy ones in high accuracy (85\%). As
this pre-processing method makes the data notably compact but also include the
feature that is needed for the classifier, it seems that alphabet frequencies
contains some useful features for understanding complex context or meaning of
the original text.
