Extraction of Person Entities Affiliated with Telkom University using Long Short-Term Memory (LSTM) on Related News Articles

News text is text that contains information about an incident that is submitted in writing or not. In the news text there is a lot of unstructured information and makes it difficult for people to know what entities are contained in the news text. To overcome these problems, information extraction is carried out which is useful for adapting information entities in the form of structured text. In this study, information extraction was carried out using the Long Short-Term Memory (LSTM) method in a news data related to Telkom University. Therefore, in this study, the development of an information extraction system was carried out by focusing on the Person entity using the LSTM method. The test results obtained an F1 Score of 81% with a tuning parameter 256 word embedding size, 32 batch, 150 epochs, 0.1 dropout, 150 units, and 1 layer on LSTM.