Exploration of Opinion Movers Interaction Patterns in the Social Network Analysis Method on Twitter (Case Study: keyword kabinet indonesia maju) - Muhamad Andre Akbar; Dian Prawira; Syahru Rahmayudha (Universitas Tanjungpura, 2022)
The development of internet media and social networks from year to year has increased resulting in a lot of data and information that can be managed into useful information in the application of several analytical methods. One of the studies that studies the relationship of social interaction is Social Network Analysis (SNA). This research was conducted by visualizing the SNA method into a python program by retrieving data on Twitter to obtain information and explore interactions and opinion drivers on Twitter. Research using the keyword Indonesiamaju. The calculation results of the Bernadgh account program are keyplayers, with a value of degree centrality 228, betweenness centrality 0.99, closenness centrality 0.87, and eigenvector centrality 0.707. This program has been tested for blackbox testing by running program functions such as crawling twitter data, input data, datasets, making edges and nodes, displaying a graph, sorted degree centrality, sorted betwenness centrality, sorted closeness centrality, and sorted eigenvector centrality. The result of this research is a program that is able to run the social network analysis method in accordance with the program flow that has been made