Analisis Sentimen Terhadap Implementasi Program Merdeka Belajar Kampus Merdeka Menggunakan Naïve Bayes, K-Nearest Neighboars Dan Decision Tree

Abstract

The development of technology are getting faster must be accompanied by the ability to adapt to changes from manual to digital, in the field of education. The Industrial internships, independent projects, student exchanges, community service projects, humanitarian initiatives, and other programs are part of the Merdeka Belajar - Kampus Merdeka program, which was launched by the Ministry of Education and Culture. The various of Merdeka Belajar -Kampus Merdeka program got a variety of responses from the public, including positive, negative, and neutral remarks posted on social media. The existence of these comments is able to create a growing sentiment among the general public and academics. Based on these problems, the researchers used twitter comments as a data source to conduct a sentiment analysis on the implementation of their Merdeka Belajar - Kampus Merdeka program. The data from Twitter will be categorized into positive, negative, and neutral classes using the Naïve Bayes method, K-Nearest Neighboars, and Decision Tree. There was a total of 475 data points divided into two groups: training data and testing data. The training data accounts for 80% of the entire data, while the remaining 20% is used for testing, with an accuracy of 99.22% for Naïve Bayes, 96.90% for K-Nearest Neighboars, and 37.21% for Decision.