<![CDATA[COVID-19 has shaken the world since the end of 2019. There are still many people who are not aware of the risk of COVID-19 despite the updated information. To prevent misinformation, trusted access is required. The amount of information provided in an information access is also not small in numbers. From those problems, a system is needed that can make it easier for people to find desired information in accessing the information provided. One system that suitable for the problem is COVID-19 articles information retrieval based on keywords provided by users. The method that can be used to build an article information retrieval is the Vector Space Model combined with Query Expansion using Word2Vec. The stages of article information retrieval system are pre-processing the dataset, word weighting, training the Word2Vec model, performing Query Expansion, calculating similarity between document and query, and sorting the document articles. The process will produce 10 news article documents related to COVID-19 that have similarities between its content and the keyword from user, the test results that get the best precision@10 and recall@10 is when the system uses 500 hidden neuron for Word2Vec training and 40 words added at the Query Expansion stage.]]>
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