During the Coronavirus pandemic, social media played an essential role in disseminating information online by utilizing various message frames and hashtags. Instagram is a popular social media worldwide that are used to spread various information. This study applied the social network analysis (SNA) approach as a theoretical framework and explored how the relationship between users on the Instagram platform related to Coronavirus outbreaks. Moreover, we also investigate how the relationship between users and their hashtags. SNA techniques were used for visualizing network models using an undirected graph, measuring network attributes, and centrality measures to find the most influential users in the network. A total of 10,403 posts based on #wabahcorona on Instagram from February 28, 2020, to May 18, 2020, were analyzed. Based on the analysis results, hashtags play an important role in this topic. Degree centrality measure as connectivity number shows that only two user accounts can make it into the list’s top ten. When using the Eigenvector centrality measure, there are no users in the top ten. The modularity measure detects 122 distinct communities that show the dense connection of nodes in the networks. Betweenness centrality measure shows that there are six related hashtags to COVID-19 pandemic out of ten hashtags. They are #wabahcorona, #covid19, #corona, #viruscorona, #dirumahaja, and #coronavirus and the four are Islamic related hashtags. The Islamic related hashtags are caused by the date of Ramadhan month.
In December 2016, Bank Indonesia (BI) officially launched the 2016 Year Emission Rupiah. With the development of technology, the process of buying and selling are not only possible between humans and humans, but humans with a machine. In addition, the machine must also be able to read and recognize the nominal banknotes in various variations of face and rotation. This is because humans can put money in machines with various variations of face and rotation. This study aims to apply and analyze the level of accuracy of nominal rupiah banknotes identification with the SURF and FLANN methods for rotation variation. Testing for identification of nominal rupiah banknotes is carried out with different rotation variations, namely 0o, 90o, 180o, and 270o. The proposed identification method provides 100% of accuracy.
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