COVID-19 (coronavirus disease 2019) is a large family of viruses that cause mild to severe illness, such as the common cold or colds and serious illnesses such as MERS and SARS. COVID-19 has become a pandemic, meaning that there has been an increase in cases of the disease which is quite fast and there has been spread between countries and caused enormous losses in various countries. The increasing number of COVID-19 cases every day in Indonesia, including in Bali Province and the resulting losses underlie the forecasting of the number of COVID-19 in Bali Province. Forecasting is carried out using the Neural Network algorithm for time series data on the number of COVID-19 in Bali Province. The data used is data on the number of COVID-19 in the Bali Province in the form of time series data sourced from the Bali Provincial Health Office. The entire forecasting process uses the Rapidminer Studio tools starting from preprocessing, modeling, testing and validation. The results of the RMSE (Root Mean Square Error) evaluation value based on testing for the positive patients were 18.956, the patients recovered were 15.413, the patients under treatment were 5.066 and the patients who died was 0.233.

Data security issues are an important aspect of data and information communication over networks. In addition, it is also necessary to look at the security side of the software. In addition to software, computers have an internet protocol in the form of HTTP which is commonly used to access websites. On the LMS website, students have access to lecture materials, discussion forums with lecturers and access to assignments given by lecturers. Wireshark is used to analyze network protocols, can log all packets going through and display detailed data. The purpose of this study is to use a Wireshark application to sniff LMS and pinpoint vulnerabilities in the system. The results of the sniffing process carried out using Wireshark on an LMS that uses the HTTP protocol clearly indicate the absence of encryption and expose the risk of vulnerabilities to the system. Recommendations given to LMS are the use of HTTPS protocol, implementation of Multi Factor Authentication, website log monitoring and password management. Recommended password management are periodic password changes, standardization policies for the use of characters in passwords and password hashing. It is hoped that when the recommendations are implemented it will improve security on the LMS website and reduce risks in data communications