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

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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.


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

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M. Ghiassi and S. Lee, "A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach," Expert Syst. Appl., vol. 106, pp. 197–216, 2018, doi:

Aryum Bibi et al., "A Novel Unsupervised Ensemble Framework using Concept-based Linguistic Methods and Machine Learning for Twitter Sentiment Analysis," Pattern Recognit. Lett., 2022, doi:

S. Yousefinaghani, R. Dara, S. Mubareka, A. Papadopoulos, and S. Sharif, "An analysis of COVID-19 vaccine sentiments and opinions on Twitter," Int. J. Infect. Dis., vol. 108, pp. 256–262, 2021, doi:

H. Vanam and J. Retna Raj R, "Analysis of twitter data through big data based sentiment analysis approaches," Mater. Today Proc., 2021, doi:

R. Riyanto, "Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia," J. Appl. Data Sci., vol. 2, no. 3, pp. 102–108, 2021, doi: 10.47738/jads.v2i3.40.

Q. Aini, "Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5," J. Appl. Data Sci., vol. 2, no. 4, pp. 143–156, 2021, doi: 10.47738/jads.v2i4.43.

T. Hariguna and V. Rachmawati, "Community Opinion Sentiment Analysis on Social Media Using Naive Bayes Algorithm Methods," IJIIS Int. J. Informatics Inf. Syst., vol. 2, no. 1, pp. 33–38, 2019.

A. Hananto, "COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia," IJIIS Int. J. Informatics Inf. Syst., vol. 5, no. 1, pp. 56–69, 2022, doi: 10.47738/ijiis.v5i1.126.

M. Rumelli, D. Akkuş, Ö. Kart, and Z. Isik, "Sentiment Analysis in Turkish Text with Machine Learning Algorithms," in 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 2019, pp. 1–5, doi: 10.1109/ASYU48272.2019.8946436.

P. Sharma and A. K. Sharma, "Experimental investigation of automated systems for twitter sentiment analysis to predict the public emotions using machine learning algorithms," Mater. Today Proc., 2020, doi:

J. R. Saura, D. Ribeiro-Soriano, and P. Zegarra Saldaña, "Exploring the challenges of remote work on Twitter users' sentiments: From digital technology development to a post-pandemic era," J. Bus. Res., vol. 142, pp. 242–254, 2022, doi:

S. H. Biradar, J. V Gorabal, and G. Gupta, "Machine learning tool for exploring sentiment analysis on twitter data," Mater. Today Proc., vol. 56, pp. 1927–1934, 2022, doi:

T. Astuti and I. Pratika, "Product Review Sentiment Analysis by Artificial Neural Network Algorithm," IJIIS Int. J. Informatics Inf. Syst., vol. 2, no. 2, pp. 61–66, 2019, doi: 10.47738/ijiis.v2i2.15.

S. Liu and J. Liu, "Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis," Vaccine, vol. 39, no. 39, pp. 5499–5505, 2021, doi:

R. Endsuy, "Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets," J. Appl. Data Sci., vol. 2, no. 1, pp. 8–18, 2021, doi: 10.47738/jads.v2i1.17.

T. Hariguna, W. M. Baihaqi, and A. Nurwanti, "Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm," IJIIS Int. J. Informatics Inf. Syst., vol. 2, no. 2, pp. 48–55, 2019, doi: 10.47738/ijiis.v2i2.13.

A. Perti, M. C. Trivedi, and A. Sinha, "Development of intelligent model for twitter sentiment analysis," Mater. Today Proc., vol. 33, pp. 4515–4519, 2020, doi:

T. H. Jaya Hidayat, Y. Ruldeviyani, A. R. Aditama, G. R. Madya, A. W. Nugraha, and M. W. Adisaputra, "Sentiment analysis of twitter data related to Rinca Island development using Doc2Vec and SVM and logistic regression as classifier," Procedia Comput. Sci., vol. 197, pp. 660–667, 2022, doi:

T. Hariguna, H. T. Sukmana, and J. Il Kim, “Survey Opinion using Sentiment Analysis,” J. Appl. Data Sci., vol. 1, no. 1, pp. 35–40, 2020.

Z. Bokaee Nezhad and M. A. Deihimi, "Twitter sentiment analysis from Iran about COVID 19 vaccine," Diabetes Metab. Syndr. Clin. Res. Rev., vol. 16, no. 1, p. 102367, 2022, doi:

D. Sunitha, R. K. Patra, N. V Babu, A. Suresh, and S. C. Gupta, "Twitter Sentiment Analysis Using Ensemble based Deep Learning Model towards COVID-19 in India and European Countries," Pattern Recognit. Lett., 2022, doi:


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