<![CDATA[This research develops an Automatic Question Answering System based on sentence relevance ranking using BERT model and SQuAD dataset. Performance evaluation is done with F1 Score and Exact Match to assess the accuracy and precision of the answers. This research includes four main approaches: question understanding and keyword identification, sentence relevance ranking using techniques such as cosine similarity or TF-IDF score, use of BERT model to enrich text representation and understand the context in depth, and performance evaluation with F1 Score and Exact Match. The results show F1 Score value of 0.6 and Exact Match value of 0.5. The research objective is to develop a system that excels in answering questions with more accurate and contextualised sentence relevance. The main contribution of this research is the advancement in natural language processing (NLP) by integrating the BERT model, SQuAD dataset, and performance evaluation using rigorous metrics. The system is expected to improve users' access to information with more precise and contextualised answers.]]>
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