Sentiment Analysis of the Convict Assimilation Program on Handling Covid-19

Abstract

Coronavirus Disease-19 (Covid-19) is an infectious disease caused by the SARS-CoV-2 virus. The rapid spread of this disease has affected 216 other countries and regions, including Indonesia. In minimizing the spread and increasing losses, it is necessary to have several policies made by the Indonesian government in dealing with this. One of the policies taken by the government is the Convict Assimilation Program to prevent the spread of the virus in prisons. The Prisoner Assimilation Program fosters inmates by integrating prisoners into social life. Many media reported on the assimilation program in various media, including news portals, so that it became a forum for the public to express their opinions. News portals can be a source for getting public opinion. Therefore, sentiment analysis can be done to determine the sentiment of any existing public opinion. In this study, the analysis was carried out by applying one of the data mining methods, namely the Support Vector Machine, with positive, negative, and neutral sentiment labeling. The data used is audience comments in Indonesian with a dataset of 404 comments and then resampled so that the number of data becomes 669. The analysis uses the kernel Radial Basis Function (RBF), RBF with Grid Search, Polynomials, and Polynomials with grid search. Kernel RBF and Kernel Polynomial with Grid Search comparing test and training data 80%:20% with the highest accuracy of 95%.

Keywords

• covid-19, sentiment analysis, support vector machine, rbf, polynomial