PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

Abstract

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86