Sentiment Analysis of Online Lectures using K-Nearest Neighbors based on Feature Selection

Junadhi; Agustin; Mi’rajul Rifqi; M. Khairul Anam

doi:10.23887/janapati.v11i3.51531

Authors

Junadhi STMIK Amik Riau
Agustin STMIK Amik Riau
Mi’rajul Rifqi Universitas Pasir Pengaraian
M. Khairul Anam STMIK Amik Riau

DOI:

https://doi.org/10.23887/janapati.v11i3.51531

Keywords:

Online lectures, Sentiment analysis, K-NN, Feature Selection, tweets

Abstract

Online lecture is a distance learning system that utilizes information technology in its implementation. Although it has been agreed, this lecture system has caused controversy. Not infrequently online lectures are considered to bring a variety of new obstacles in lectures, and not a few also consider that online lectures are the most appropriate solution to continue to run lecture activities in the midst of alarming pandemic conditions. In response to this policy, many people expressed various kinds of opinions and views on the implementation of online lectures which are generally stated on social media, one of which is through Twitter. Sentiment analysis is a branch of the science of machine learning that is carried out to obtain useful information or new knowledge by extracting, understanding, and processing text data automatically. Several methods are widely used by researchers to classify sentiment analysis datasets including K-Nearest Neighbor (K-NN). K-NN will be adapted to classify online lecture datasets because K-NN can produce good accuracy on a large number of data. The presence of feature selection also helps machine learning in improving its performance. The purpose of this study was to determine student sentiment toward online lectures and to determine the level of accuracy of the combination of K-NN with various feature selections. Based on 780 tweets data, a classification of 394 positive sentiments, 320 negative sentiments, and 39 neutral sentiments was obtained. So, the results of student opinions are on POSITIVE sentiments. The accuracy result of the K-NN Algorithm was 56% and the accuracy of the K-NN Algorithm + Forward Selection was 51%, the accuracy of the KNN Algorithm + Adabost was 54%, and the accuracy of the KNN Algorithm + Genetic Algorithm was 55%.

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