ANALISIS SENTIMEN PENGARUH PEMBELAJARAN DARING TERHADAP MOTIVASI BELAJAR DI MASA PANDEMI MENGGUNAKAN NAIVE BAYES DAN SVM

Abstract

The COVID-19 pandemic in Indonesia has had a huge impact on the education sector. Where it is today, it must implement and adapt a new learning model called online. motivation in learning is very important because it can improve achievements. In this case there are many pros and cons about online learning, many people's opinions on social media, especially Twitter about the influence of online learning on learning motivation. This study aims to analyze the influence between online learning and learning motivation. Public opinion on Twitter is used as a sentiment analysis to find out what people think about online learning on learning motivations whether positive or negative. The data used are tweets in indonesian with the keywords "online learning", "distance learning" and "motivational learning", with the number of datasets as many as 455 tweets are classified into 2 parts namely agreement and disagreement. The classification in this study used naive bayes classification algorithm method and Support Vector Machine (SVM) by preprocessing data using tokenize, transform case, filtering and stemming. Data is processed using rapidminer application. The highest accuracy result of this study was by the classification algorithm method support vector machine (SVM) with accuracy 97.22%, precision 94.72%, recall 100% and error 2.78%.