Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning

  • Ahmad Rais Dwijaya (1) Universitas Amikom Yogyakarta
  • Arif Dwi Laksito (2*) Universitas Amikom Yogyakarta

  • (*) Corresponding Author
Keywords: Machine Learning, Deep Learning, Sentiment Analysis, LSTM


The COVID-19 pandemic that hit the world at the end of early 2020 caused many losses. The Indonesian government has established various ways to reduce the path of the COVID-19 pandemic by launching the PeduliLindungi application to reduce the spread of COVID-19. Various layers of society responded to the launch of the application with various opinions. This research mainly analyzes public opinion sentiment toward the PeduliLindungi application, as determined by 10,000 reviews on the Google Play Store. This study aims to compare the performance of deep learning and machine learning models in sentiment analysis. The stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word TF-IDF, which includes the Nave Bayes algorithm, Decision Tree, Random Forest, K-Nearest Neighbour, and SVM. As for the deep learning model with the fastText word embedding word representation technique using the LSTM algorithm, an evaluation is carried out using the confusion matrix. The results of this study state that deep learning models perform better than machine learning models.


Download data is not yet available.


Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018). Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes. 2018 International Conference on Orange Technologies, ICOT 2018, 1–6.

Dashtipour, K., Gogate, M., Adeel, A., Larijani, H., & Hussain, A. (2021). Sentiment analysis of persian movie reviews using deep learning. Entropy, 23(5), 1–16.

Deho, O. B., Agangiba, W. A., Aryeh, F. L., & Ansah, J. A. (2018). Sentiment analysis with word embedding. IEEE International Conference on Adaptive Science and Technology, ICAST, 2018-Augus(August), 1–4.

Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M. (2020). A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews. 2020 International Conference on Contemporary Computing and Applications, IC3A 2020, 217–220.

Djalante, R., Lassa, J., Setiamarga, D., Sudjatma, A., Indrawan, M., Haryanto, B., … Warsilah, H. (2020). Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, 6.

Feizollah, A., Ainin, S., Anuar, N. B., Abdullah, N. A. B., & Hazim, M. (2019). Halal Products on Twitter: Data Extraction and Sentiment Analysis Using Stack of Deep Learning Algorithms. IEEE Access, 7, 83354–83362.

Firmansyah, I., Asnawi, M. H., Hasanah, S. A., Novian, R., & Pravitasari, A. A. (2021). A Comparison of Support Vector Machine and Naïve Bayes Classifier in Binary Sentiment Reviews for PeduliLindungi Application. 2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021, (18), 140–145.

Kapočiūtė-Dzikienė, J., Damaševičius, R., & Woźniak, M. (2019). Sentiment analysis of Lithuanian texts using traditional and deep learning approaches. Computers, 8(1).

Keputusan Menteri Kesehatan Republik Indonesia. (2020). Keputusan Menteri Kesehatan Republik Indonesia Nomor HK.01.07/MenKes/413/2020 Tentang Pedoman Pencegahan dan Pengendalian Corona Virus Disease 2019 (Covid-19). MenKes/413/2020, 2019, 207.

Kilimci, Z. H. (2020). Sentiment analysis based direction prediction in bitcoin using deep learning algorithms and word embedding models. International Journal of Intelligent Systems and Applications in Engineering, 8(2), 60–65.

Kilimci, Z. H., & Akyokus, S. (2019). The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification. UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering, 548–553.

Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., & Joulin, A. (2018). Advances in pre-training distributed word representations. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 52–55. Miyazaki: European Language Resources Association (ELRA). Retrieved from

Ombabi, A. H., Ouarda, W., & Alimi, A. M. (2020). Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks. Social Network Analysis and Mining, 10(1), 1–13.

Onan, A. (2021). Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency and Computation: Practice and Experience, 33(23), 1–12.

Pribadi, M. R., Manongga, D., Purnomo, H. D., Setyawan, I., & Hendry. (2022). Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE. 2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding, 115–119.

Robertson, S. (2004). Understanding inverse document frequency: On theoretical arguments for IDF. Journal of Documentation, 60(5), 503–520.

Romadhoni, Y., Fahmi, K., & Holle, H. (2022). Analisis Sentimen Terhadap PERMENDIKBUD No.30 pada Media Sosial Twitter Menggunakan Metode Naive Bayes dan LSTM. Jurnal Informatika: Jurnal Pengembangan IT (JPIT), 7(2), 118–124.

Steinke, I., Wier, J., Simon, L., & Seetan, R. (2022). Sentiment Analysis of Online Movie Reviews using Machine Learning. International Journal of Advanced Computer Science and Applications, 13(9), 618–624.

Stephenie, Warsito, B., & Prahutama, A. (2020). Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method. E3S Web of Conferences, 202, 1–10.

Sudiarsa, I. W., & Wiraditya, I. G. B. (2020). Analisis Usability Pada Aplikasi Peduli Lindungi Sebagai Aplikasi Informasi Dan Tracking Covid-19 Dengan Heuristic Evaluation. INTECOMS: Journal of Information Technology and Computer Science, 3(2), 354–364.

Tran, D. D., Nguyen, T. T. S., & Dao, T. H. C. (2022). Sentiment Analysis of Movie Reviews Using Machine Learning Techniques. In Lecture Notes in Networks and Systems (Vol. 235). Springer Singapore.

Zahoor, K., Bawany, N. Z., & Hamid, S. (2020). Sentiment analysis and classification of restaurant reviews using machine learning. Proceedings - 2020 21st International Arab Conference on Information Technology, ACIT 2020.

How to Cite
Dwijaya, A., & Laksito, A. (2023). Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning. Jurnal Riset Informatika, 5(2), 187-194.
Article Metrics

Abstract viewed = 259 times
PDF downloaded = 128 times