Relevance of the Retrieval of Hadith Information (RoHI) using Bidirectional Encoder Representations from Transformers (BERT) in religious education media

This research explores the impact of integrating Bidirectional Encoder Representations from Transformers (BERT) into the Retrieval of Hadith Information (RoHI) application within the realm of religious education media. Hadith, the sayings and actions of Prophet Muhammad, play a pivotal role in Islamic teachings, requiring accurate and contextually relevant retrieval for educational purposes. RoHI, designed to enhance access and comprehension of Hadith literature, employs BERT's advanced natural language processing capabilities. The study assesses how BERT-enhanced RoHI facilitates efficient retrieval and interpretation of Hadith texts. By leveraging BERT's ability to capture intricate language patterns and semantics, the study aims to enhance the precision and contextual appropriateness of retrieved Hadith information. The study also discusses implications for digital learning platforms, emphasizing the potential of NLP technologies to foster broader access to religious knowledge and promote inclusive educational practices. This research contributes to the field by proposing a framework that integrates advanced AI techniques with religious education, ensuring that learners receive accurate and meaningful Hadith information tailored to their educational needs. The findings highlight the potential of BERT in revolutionizing information retrieval processes in religious studies, paving the way for more effective educational tools and resources in Islamic learning environments.