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On 2 March 2020, the Indonesian government, through President Joko ‘Jokowi’ Widodo, announced the first two cases of COVID-19 in Indonesia. This is the first case of COVID-19 officially confirmed in that country. Several cases have continued to increase since then. President Jokowi began issuing policies on the spread of this virus. This is different from other countries, such as Malaysia and Singapore, which responded from the previous month when the Indonesian government still stated that coronavirus does not exist in Indonesia. Our case study is to find a public opinion through social network analysis of Indonesian public policy during the beginning of the Indonesian COVID-19 pandemic in March 2020. This research implements text mining and document-based sentiments on Twitter data that is reprocessed through machine learning techniques using the Naïve Bayes method. We found negative opinions in the period more dominant by 46%, while that was 35% positive sentiment and 20% neutral. This research shows that anticipation, sadness, and anger are very dominant in the emotional analysis.

Traveloka is currently the most popular startup in Indonesia with share traffic reaching 78.49% using smartphone and monthly visits whichreached 28.92 million based on a report in similarweb.com in May 2019. Traveloka, based on record, has been downloaded 10 million times since 2014with rating reaches 4.4 out of 5 stars. As of May 2019, there were 386,646 reviews from users in the PlayStore, ranging from positive and negativereviews. However, it is necessary to analyze with certain methods to summarize the review. Every review given will get a conclusion after collected, andsentiment analysis will provide user experiences from the Traveloka application within certain period. This research was conducted using the NaïveBayes Classifier method based on a review from the playstore to determine service quality. The purpose of this study is to find out the perceptions ofusers based on the measurement of service quality so that the results can be an evaluation for Traveloka in improving services. Studies show that duringthis period public opinion produced negative sentiments with Vmap value of 0.31020 greater than positive sentiment with a value of 0.16132.

With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.