Muhammad Arif Bijaksana
Telkom University

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Sequence Chunking on Quran in English Translation using Bidirectional Long Short-Term Memory Try Arie; Muhammad Arif Bijaksana
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 2 (2020): September, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.2.492

Abstract

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.