Analisis Sentimen Mahasiswa di Surabaya Terhadap Pelayanan Vaksinasi COVID-19 Menggunakan Beberapa Classifier

Meliana Kusuma Pangkasidhi, Henry Novianus Palit, Andre Gunawan


Indonesia is one of the countries that are currently struggling to deal with the COVID-19 virus pandemic by providing vaccination. The government is currently trying to persuade the public to do vaccination by maximizing COVID-19 vaccination services. In reality, vaccination services still have problems with some aspects. To see various insights on vaccination services that have been implemented, therefore a research was conducted in the field of sentiment analysis to analyze public opinion. In this research, classifiers that will be used are Naïve Bayes, Support Vector Machine (SVM), Random Forest, and Light Gradient Boosting Machine (LGBM) to perform text classification and their performances will be compared with evaluation metrics. There are two types of datasets used, namely questionnaire dataset and social media dataset. The questionnaire model will be tested using a social media dataset, while the social media model will use social media dataset that will be split. The testing results show that the model trained with the social media dataset produces better performance than the questionnaire model. Of these four classifiers, the best model for aspect and sentiment classification is Random Forest


sentiment analysis; text classification; vaccination; COVID-19; naïve bayes; SVM; random forest; LGBM

Full Text:



Alzamzami, F., Hoda, M., & Saddik, A. E. 2020. Light

Gradient Boosting Machine for General Sentiment

Classification on Short Texts: A Comparative Evaluation.

Retrieved from


Athiwaratkun, B., Wilson, A. G., & Anandkumar, A. 2018.

Probabilistic FastText Multi-Sense Word Embeddings.

Retrieved from

Baid, P., Gupta, A., & Chaplot, N. 2017. Sentiment Analysis

of Movie Reviews using Machine Learning Techniques. From


Charlyn, et al. 2021. Twitter Sentiment Analysis towards

COVID-19 Vaccines in the Philippines Using Naive Bayes.

NN. 2021. Cara Memperbaiki Data Sertifikat Vaksin yang

Salah, Jangan Panik Dulu!. Retrieved from

Dey, et al. 2016. Sentiment Analysis of Review Datasets using

Naïve Bayes’ and K-NN Classifier.

Ke, G., et al. 2017. LightGBM: A Highly Efficient Gradient

Boosting Decision Tree. Retrieved from

Khanvilkar & Vora. 2018. Sentiment Analysis for Product

Recommendation Using Random Forest. Retrieved from

Mountassir, A., Benbrahim, H. & Berrada, I. 2012. An

empirical study to address the problem of Unbalanced Data

Sets in sentiment classification. IEEE 2012 IEEE International

Conference.doi:10.1109/ICS MC.2012.6378300

Musyaddad, A. A. 2021. Ombudsman Temukan Sejumlah

Masalah Vaksinasi Covid di Surabaya. Retrieved from


Priyavrat, & Singh, A. J. 2017. Sentiment Analysis: A

Comparative Study of Supervised Machine Learning

Algorithms Using Rapid miner. Int. J. Res. Appl. Sci. Eng.

Technol., vol. 5, pp. 80–89.

Pristiyono, et al. 2020. Sentiment analysis of COVID-19

vaccine in Indonesia using Naïve Bayes Algorithm. IOP

Conference Series: Materials Science and Engineering.

Retrieved from

-899X/1088/1/ 012045/meta

Prusa, J., Khoshgoftaar, T. M., Dittman, D. J. & Napolitano,

A. 2015. Using Random Undersampling to Alleviate Class

Imbalance on Tweet Sentiment Data. IEEE 2015 IEEE

International Conference. doi:10.1109/IRI.2015.39

Salman, Ghinan. 2021. Punya Keluhan Soal Sertifikat Vaksin

COVID-19, Warga Surabaya Bisa Lapor ke Layanan Ini.

Retrieved from


Scikit-learn Developers. 2021. MultinomialNB. Retrieved



Somantri, O., & Apriliani, D. 2018. Support Vector Machine

Berbasis Feature Selection Untuk Sentiment Analysis

Kepuasan Pelanggan Terhadap Pelayanan Warung Dan

Restoran Kuliner Kota Tegal.


Ting, S.L, Ip, W. H., & Tsang, A. H. C. 2011. Is Naive Bayes

a Good Classifier for Document Classification?. Retrieved


Young, J. C., & Rusli, A. 2019. Review and Visualization of

Facebook’s FastText Pretrained Word Vector Model.



  • There are currently no refbacks.

Jurnal telah terindeks oleh :