Sentiment Analysis using the Support Vector Machine Algorithm on Covid_19 - Nugroho, Adytyo Wahyu; Norhikmah, Norhikmah (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2024)
This massive development of information technology makes it easier for people's lives in various fields, one of them is social media, social media that people use a lot to get information about news or events that are happening in Indonesia, one of which is social media Twitter which provides a lot of information for the people of Indonesia, one of which is information about Covid-19 which is currently rife in the territory of Indonesia Sentiment analysis is a branch of Natural Language Processing (NLP) which can help determine the sentiments that occur in society. This study uses data in the form of tweets to carry out sentiment analysis obtained on Twitter social media.This research utilizes one of the Supervised Learning algorithms, namely Support Vector Machine. In this study, three (3) kernels are used for the Support Vector Machine, each of which is Linear, Radial basis function and Polynomial, to find which kernel produces the highest accuracy value. From the experiments carried out using data sharing for training as much as 70% and for testing data as much as 30% of the total data of 6000 data, the resulting accuracy value for the Support Vector Machine method on the Linear kernel produces an accuracy value of 89% and for the Radial kernel base function accuracy by 90% and for the Polynomial kernel it produces an accuracy of 88%. So it is concluded for the three (3) kernels for testing the Support Vector Machine method on the Radial basis function kernel to produce the best accuracy value