Reducing Data Social Network Utilizing Greedy Randomized Method - Muhammad Rizqy Alfarisi; Andry Fajar Zulkarnain; Andry Alamsyah (Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa, 2022)
There are many complex data in social networks that can be combined into large data sets which can be accessed. Large amounts of data require more storage and increase the computed summary costs. The need to know how to use data effectively and to extract information from reduced data that has the same information as super data before being reduced. the first thing to do is to convert any unstructured data into structured data utilizing the greedy randomized method, the data can be grouped and combined with other data in its vicinity, and the size of the data can be reduced because the node (user) grouping as a linked pair and is formed the best node around it. This paper presents how to use the minimum description length, as information theory to provide solutions in the model selection problem and apply it in a greedy randomized algorithm that can group unstructured data to reduce data size and provide visualization of the relationship between nodes and how accurate and faster greedy randomized would reduce and combined data into simple link nodes