Sequence Chunking on Quran in English Translation using Bidirectional Long Short-Term Memory

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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.

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Published
2020-10-02
How to Cite
Arie, T., & Bijaksana, M. A. (2020). Sequence Chunking on Quran in English Translation using Bidirectional Long Short-Term Memory. Indonesia Journal on Computing (Indo-JC), 5(2), 91-98. https://doi.org/10.34818/INDOJC.2020.5.2.492
Section
Computer Science