ABSTRACT
Background: Omicron has respiratory problems and pneumonia in general and specific terms. This pandemic was ravaging all countries in the world. This virus outbreak had new types to appear or so-called new variants that are still being studied by experts. Computer-assisted methods (includes smart intelligence systems, algorithms, and data mining) is key solution for detecting variants of virus. Methods: In present study, it discussed and analyzed the omicron variant which is one of the variants of the Coronavirus 2019 (COVID-19). It's a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The emergence of this Omicron variant of COVID-19, raised more concern in the world because of its dangerous ability and the high level of spread of omicron cases. Analysis using the k-means algorithm in order to determine the level of distribution of the virus variant. Result: From the results and outputs found in this method, it is concluded that this method is used to divide the data into 3 clusters of case distribution of the Omicron variant which has been understood as a level in the distribution of cases where cluster 0 is low level, cluster 1 is high level, and cluster 2 is medium level. Conclusion: Therefore, this data mining method with special clustering and data-mining techniques give the highest number of virus distributions in which countries and divide some countries into several clusters. © 2020 The author (s).
ABSTRACT
The rapid growth of the Internet and Technology produced a massive amount of data that resulted a phenomenon called Big Data. To process such a complex kind of massive amount of data, an advanced approach and tool is needed that is able to quickly produce results. This approach to analyzing massive amount of data is known as Big Data Analytics. Big data analytics is widely used in various sectors, not to mention the health sector. In the healthcare sector, recently there has been a study that is often carried out in dealing with crisis situations, namely research on implementing big data analytics to provide technological solutions to help deal with pandemics. In this article, we analyze and visualize the data collected from Indonesia. The data analyzed starts from the first case of COVID-19 in Indonesia to present. The proposed solution is to classify the regional case data into a group that can represent the situation of the area. As a result, it is determined based on the data that there are three groups consisting of areas with low risk, moderate risk, and high risk. In addition, this article proposes combining big data analytics technology with cloud technology to facilitate the dissemination of information to citizens to increase awareness about the spread of the COVID-19 virus.
ABSTRACT
The Covid-19 pandemic that has hit Indonesia since 2020 sociologically has caused various changes in the order of social life and has implications for various social changes that occur in rice farmers and their level of welfare. The specific purpose of this study is to take an inventory of various phenomena of changes in farmer behavior in organic rice agribusiness and whether there are differences in farmer behavior in the agribusiness system before and during the Covid-19 pandemic associated with their level of welfare. In the long term, this study will be used to prepare the Food Crops Agribusiness Development Model following the research roadmap of the researchers that have been carried out. The study used a survey of organic rice farmers in the East Priangan area with a total sample of 43 farmers from a population of 427 organic farmers. The data used consist of primary and secondary data. Different analyzes were used to determine the behavior of farmers in organic rice agribusiness activities before and during the covid-19 pandemic. The different analyzes were processed using the Wilcoxon statistical test. Based on the analysis, there are genuine behavioral changes in all agribusiness subsystems studied, which consist of providing production facilities, on-farm subsystem, product processing subsystem, marketing subsystem, a subsystem of supporting elements, and changes in this behavior. Also causes a decrease in the level of welfare. © 2023, University of Guilan. All rights reserved.