Implementasi Algoritma Term Frequency Inverse Document Frequency (TF-IDF) dalam Menganalisis Sentimen Masyarakat Terhadap Covid-19 Varian Omicron

Abstract

The latest variant was detected on November 24, 2021, namely the Omicron variant WHO said, Omicron was one of the Covid-19 variants that had mutated, with a very fast spread rate. The Government Republic of Indonesia has officially banned all foreigners from entering Indonesia, both those who have done so travel or come from countries exposed to the Omicron variant. This study uses data that has been processed using Netlytic online website. Netlytic analyzes text and visualizes public online conversations on social media sites. text preprocessing has several stages, namely case folding, tokenizing, stopword, stemming. Data analysis is the stage to classify words into positive, negative, or neutral sentiment classes. the last step is calculating the weights using the tf-idf method. It is proven from the DF value which reaches 628 words in one document, the D/DF value is 0.39 and the log D/DF is -0.41. The TF-IDF method can be taken in outline, namely it is easy to calculate frequency and relevance occurrence of words in a document. The TF-IDF method produces output according to user specifications, but this method takes a long time for large amounts of data.