Every Moslem is obliged to read and understand the meanings of the Quran. The problem is
the amount of information contained in the Quran so that ordinary people have difficulty
understanding the Quran as a whole. Neural networks can be used to extract important
information in the Quran to solve this problem. Therefore, the author proposes a model to
identify and classify tags using sequence chunking. The system will use the Bi-LSTM model
where the system will be given various token from the Quran as the inputs to be identified
as the correct tags. The author is using the dataset obtained from website quran.com. The
evaluation of the proposed model produces an f-measure value of 0.903.