Data Mining for Prediction of Covid-19 Patient Status using Naive Bayes Classifier


Dewi Yanti Liliana
Hata Maulana
Agus Setiawan


The Covid-19 pandemic in 2020 is a complex health problem and requires fast handling and collaborative solutions from various disciplines. Covid-19 patients who are hospitalized have different conditions and severity. This has an effect on the handling actions that will be taken by medical personnel. The large number of patients and the lack of medical personnel have resulted in the need for technology support to help classify patient status based on their conditions so that treatment is concentrated on patients who are very serious and need fast treatment. This study applies predictive techniques from data mining disciplines to classify the emergency status of patients. The Naive Bayes Classifier was applied to build a model based on a dataset of patients infected with Covid-19. The dataset of Covid-19 patients in Indonesia was obtained from and applied using RapidMiner software. The model built can predict the emergency status of patients based on age and sex who have the highest likelihood of recovering from COVID-19 and patients who have a high likelihood of continuing to undergo treatment and /or deceased. The results of this study indicates that the classification of the Naive Bayes reached 96.67% of accuracy rate in classifying patient status.


Author Biography

Dewi Yanti Liliana, Computer and Informatics Engineering, Politeknik Negeri Jakarta
How to Cite
Liliana, D. Y., Maulana, H., & Setiawan, A. (2021). Data Mining for Prediction of Covid-19 Patient Status using Naive Bayes Classifier. MULTINETICS, 7(1), 48–53. Retrieved from


  1. C. Long et al., “Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?,” Eur. J. Radiol., vol. 126, p. 108961, May 2020, doi: 10.1016/j.ejrad.2020.108961.
  2. S. Sanche, Y. T. Lin, C. Xu, E. Romero-Severson, N. Hengartner, and R. Ke, “High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2,” Emerg. Infect. Dis. J., vol. 26, no. 7, 2020, doi: 10.3201/eid2607.200282.
  3. F. Rustam et al., “COVID-19 Future Forecasting Using Supervised Machine Learning Models,” IEEE Access, vol. 8, pp. 101489–101499, 2020, doi: 10.1109/ACCESS.2020.2997311.
  4. D. Susanna, “When will the COVID-19 pandemic in indonesia end?,” Kesmas, vol. 15, no. 4, pp. 160–162, 2020, doi: 10.21109/KESMAS.V15I4.4361.
  5. M. Sukmana, M. Aminuddin, and D. Nopriyanto, “Indonesian government response in COVID-19 disaster prevention,” East Afrian Sch. J. Med. Sci., vol. 3, no. 3, pp. 81–6, 2020, doi: 10.36349/EASMS.2020.v03i03.025.
  6. “Kawalcovid.” [Online]. Available:
  7. S. K. Kar, S. M. Y. Arafat, P. Sharma, A. Dixit, M. Marthoenis, and R. Kabir, “COVID-19 pandemic and addiction: Current problems and future concerns,” Asian J. Psychiatr., vol. 51, p. 102064, 2020, doi:
  8. Y. C. Wu, C. S. Chen, and Y. J. Chan, “The outbreak of COVID-19: An overview,” J. Chinese Med. Assoc., vol. 83, no. 3, pp. 217–220, 2020, doi: 10.1097/JCMA.0000000000000270.
  9. M. Abed Alah, S. Abdeen, and V. Kehyayan, “The first few cases and fatalities of Corona Virus Disease 2019 (COVID-19) in the Eastern Mediterranean Region of the World Health Organization: A rapid review,” J. Infect. Public Health, vol. 13, no. 10, pp. 1367–1372, 2020, doi: 10.1016/j.jiph.2020.06.009.
  10. L. K. Kumar and P. J. A. Alphonse, “Automatic Diagnosis of COVID-19 Disease using Deep Convolutional Neural Network with Multi-Feature Channel from Respiratory Sound Data : Cough , Voice , and Breath Reference : To appear in : Received Date : Revised Date : Accepted Date : Abstract :,” Alexandria Eng. J., 2021, doi: 10.1016/j.aej.2021.06.024.
  11. T. Miller, “Explanation in artificial intelligence : Insights from the social sciences,” Artif. Intell., vol. 267, pp. 1–38, 2019, doi: 10.1016/j.artint.2018.07.007.
  12. A. Fattah and R. Setyadi, “Teknologi informasi dan pendidikan,” J. Teknol. Inf. dan Pendidik., vol. 12, no. 2, pp. 1–7, 2019.
  13. P. D. Utami and R. Sari, “Filtering Hoax Menggunakan Naive Bayes Classifier,” Multinetics, vol. 4, no. 1, p. 57, 2018, doi: 10.32722/vol4.no1.2018.pp57-61.
  14. Prasdika and B. Sugiantoro, “A Review Paper On Big Data And Data Mining,” Int. J. Informatics Dev., vol. 7, no. 1, pp. 33–35, 2018.
  15. M. H. Frické, “Data-Information-Knowledge-Wisdom (DIKW) Pyramid, Framework, Continuum,” in Encyclopedia of Big Data, L. A. Schintler and C. L. McNeely, Eds. Cham: Springer International Publishing, 2018, pp. 1–4.
  16. D. Y. Liliana and D. Priharsari, “Tsunami Early Warning Detection using Bayesian Classifier,” Proc. - 2019 2nd Int. Conf. Comput. Informatics Eng. Artif. Intell. Roles Ind. Revolut. 4.0, IC2IE 2019, pp. 44–48, 2019, doi: 10.1109/IC2IE47452.2019.8940823.
  17. “Kaggle.” [Online]. Available: