An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis

- Sarmini - Universitas Amikom Purwokerto, Purwokerto, 53127, Indonesia
Abdullah Alhabeeb - King Saud University, Riyadh, 11451 Saudi Arabia
Majed Mohammed Abusharhah - Ministry of Education, Saudi Arabia
Taqwa Hariguna - Universitas Amikom Purwokerto, Purwokerto, 53127, Indonesia
Andhika Rafi Hananto - Universitas Amikom Purwokerto, Purwokerto, 53127, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.3.894

Abstract


An anti-Covid-19 plan with social restrictions forced all Indonesian educational institutions to implement online learning in 2020. Strategy in early 2022, a new policy brought back online learning methods. Because of the rapid change and short adaptation period, online learning, which had been accepted as a solution for approximately two years, has become controversial. There were a variety of reactions in society, particularly on social media, after the rapid shift from face-to-face learning to online learning. This study will quantify text sentiment expressed on social media through machine learning. This study used SVM, RF, DT, LR, and k-nearest neighbors to develop a sentiment analysis model for use in sentiment research (KNN). The SVM- and RF-based sentiment analysis models outperform the others in cross-validation tests using data from the same Twitter social media site. Furthermore, RF can classify public opinion into three groups: positive, negative, and neutral, with a low error rate. The f1 values of our KNN-based model were measured at 75%, 65%, and 87% for negative, neutral, and positive tweets, respectively, which are slightly more accurate than previous studies with the same method and purpose.


Keywords


Machine learning; sentiment analysis; public opinions; E-Learning.

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References


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