K-Nearest Neighbors Algorithm to Student Opinion of the Online Learning Method at Wira Wacana Sumba Christian University

Abstract

The education sector is one of the areas that has felt the major impact of the Covid-19 pandemic. The impact that arises is the teaching and learning process must be carried out from home using the online learning method. This teaching and learning method raises a variety of responses from students. This is what makes researchers analyze these views, both in the form of positive opinions or negative opinions. The analysis process is carried out by applying sentiment analysis or opinion mining from the comment on Facebook, text mining is processed using the preprocessing method, labeled it to positive and negative. Based on the available data, a classification process is carried out using the K-Nearest Neighbors algorithm. Rapid Miner is used to experiment text data with the KNN algorithm in order to find the value of accuracy, precision and recall. From the results of research, it was obtained a value of 87.00% for accuracy and 0.916 for the AUC value. The values ​​are high enough for the classification of student opinion against this pandemic so that this research is classified as Excellent Classification.