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Abstract
Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) is a novel
virus that causes a global pandemic Coronavirus Disease 2019 (Covid-19). The spread of the
COVID-19 pandemic was very fast through direct contact, so the government issued various
policies to suppress the spread of Covid-19. Extension policies set by the government caused
various responses, especially the dine-in regulation is a maximum of 20 minutes. Therefore,
researchers predict the length of time a person eats and analyze the sentiment of Generation Z
in order to apply data science in decision making.
Methods: This study uses primary and secondary data. Primary data was obtained by
distributing questionnaires through Google Form and analyzed using the Monte-Carlo method,
while secondary data was obtained by scraping Twitter data and analyzed using sentiment
analysis.
Results: Based on the simulation carried out 10 times on the length of time eating at home and
eating dine-in alone using the Monte-Carlo method, the prediction results obtained the average
length of time eating for 17 minutes and 25 minutes with an accuracy rate of 96,393
respectively. % and 94,799%. Meanwhile, data testing using MAPE shows a prediction error
rate of 3.472% for eating at home and 3.052% for dine-in alone, so it can be stated that the
prediction success is 96.268% and 96.948%, respectively. These results indicate that the
simulation results that have been carried out are accurate.
Conclusions: Sentiment analysis results were also obtained, namely negative responses of
34.13%, positive of 30.2%, and neutral of 33.99%. Based on the negative views obtained from
sentiment analysis on the PPKM dine-in policy, and the simulation results of the Monte Carlo
method, there is a need for a review for the government in making policies.
Keywords: Monte-Carlo, Sentiment Analysis, PPKM, COVID-19.
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