https://socictopen.socict.org/files/original/7bf0b736b50559d5af902d4f69bb8c01.pdf 96aa7d1e10dfbec2f3a364229963ae3b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning Creator An entity primarily responsible for making the resource Novrindah Alvi Hasanah, Nanik Suciati, Diana Purwitasari Description An account of the resource Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, deep learning, twitter, Word Embedding, monitoring public concern Identifier An unambiguous reference to the resource within a given context 10.29207/resti.v5i1.2927 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Information technology, Systems engineering