Abstract
Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people. However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challenging to distinguish between authentic and fabricated Hadiths. In terms of Hadith science, determining the authenticity of a Hadith can be accomplished by examining its Sanad and Matn. Sanad is an essential aspect of the Hadith because it indicates the chain of the Narrator who transmits the Hadith. The research reported in this paper provides an advanced Natural Language Processing (NLP) technique for identifying and authenticating the Narrator of Hadith as a part of Sanad, utilizing Named Entity Recognition (NER) to address the necessity of authenticating the Hadith. The NER technique described in the research adds an extra feed-forward classifier to the last layer of the pre-trained BERT model. In the testing process using Cahya/bert-base-indonesian-1.5G, the proposed solution received an overall F1-score of 99.63 percent. On the Hadith Narrator Identification using other Hadith passages, the final examination yielded a 98.27 percent F1-score.
Highlights
Islam is a massive faith globally, with over 2 billion Muslims in 2018, accounting for approximately 29.04% of global society
This study proposes semi-supervised BERT (Bidirectional Encoder Representations from Transformers) with an extra feed-forward neural network for Hadith Narrators to execute Named Entity Recognition (NER), for Indonesian Hadith texts
The BERT-BGRU-Conditional Random Fields (CRF) model outperformed the other models with an F-measure of 94.76 percent on the CANERCorpus
Summary
Islam is a massive faith globally, with over 2 billion Muslims in 2018, accounting for approximately 29.04% of global society. Recognizing the narrators' names has a crucial role in authorizing a particular Hadith. NER is an NLP role that recognizes and classifies named entities in a provided text. It is challenging to address NER to identify the Hadith Narrator and authenticate it. This study proposes semi-supervised BERT (Bidirectional Encoder Representations from Transformers) with an extra feed-forward neural network for Hadith Narrators to execute NER, for Indonesian Hadith texts. In case all of the Hadith Narrators have already been identified using the proposed NER Model.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
