ABSTRACT
The government's policy in dealing with the Covid-19 pandemic that has entered Indonesia has generated a response from the public, including Twitter social media users. Responses or comments from the community are also very religious, ranging from positive responses in supporting government policies in dealing with this Covid-19 to negative reactions in the form of criticism of the government, which is considered to have underestimated this virus. We use sentiment analysis to determine whether an opinion or comment containing a statement is positive or negative. The comments are rated by Indonesian language experts. In this study, the sentiment analysis process uses the TF-IDF method for word weighting, the information gain method for feature selection, and the Naive Bayes method for classifying public opinion. The results of this study indicate that the Naive Bayes algorithm is quite good in the text data classification model, with the highest accuracy, precision, and recall levels of 87.0%, 89.0%, and 98.0%, respectively. The average accuracy, precision, and recall levels are 86.2%, 87.6%, and 97%, respectively. This research can provide accurate and valuable information for the community regarding the response of Twitter users to the policy of handling Covid-19 in Indonesia. © 2022 IEEE.
ABSTRACT
Background: COVID-19 is a disease caused by infection with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). COVID-19 is still a worldwide threat because of its high morbidity and mortality. This is influenced by the occurrence of hypertension, obesity, age and diabetes mellitus. However, currently there is still controversy in the results of research regarding the use of metformin in COVID-19 with type 2 diabetes mellitus (type 2 DM). This study was aimed to analyze the effect of metformin in COVID-19 patients with diabetes mellitus on mortality rates. Subjects and Method: This study was a systematic review and meta-analysis with the following PICO. P: COVID-19 patient with type-2 diabetes mellitus. I: administration of metformin therapy. C: therapy other than metformin and O: mortality. The articles used in this study were obtained from several databases, namely PubMed, Science Direct, Proquest, SpringerLink, Google Scholar and Scopus. The article search keywords were: "COVID-19" OR "coronavirus" AND "diabetes" AND "metformin" AND "mortality". Articles included are full-text English using a cohort study design from 2020 to 2021 and reporting the Odds Ratio in multivariate analysis. The selection of articles was carried out using the PRISMA flow chart. The articles were analyzed using the Review Manager 5.3 application.