The Quran is a holy book for Muslims all over the world. Therefore, the Quran is not only translated into Indonesian but also into many other languages, including English. The contents of the Quran are a collection of thousands of verses, each verse having different topics and entities. Sometimes, someone may find it difficult to understand and study the contents of the Quran. Therefore, to make it easier, it is done by extracting information and identifying various entities in the Quran, such as human entities. An important thing to do in order to extract information on human entities is to extract information related to the human entity itself first. Because it can help in the search process, particularly the search for names of people in the Quran. The extraction of human entities is commonly known as Named Entity Recognition (NER). With NER, it can automatically recognize important entities such as people's names, group names, and other entities in a sentence or verse in the Quran. Currently, research on the Quran's English translation is not widely done. Therefore, in this research, we are building an information extraction system model for human entities based on a pre-trained deep learning model called Bidirectional Encoder Representations from Transformer (BERT). The dataset used is made up of 19473 tokens and 720 entities taken from the website tanzil.net. The development of the model shows that BERT can be used to extract information for NER on the Quran translation in English by obtaining a F1-score value of 53 %.