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Muhammad Fauzi
Universitas Muhammadiyah Jambi, Jambi
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Prediksi Penyebaran Virus COVID-19 Dari Hasil PCR Menggunakan Metode Naïve Bayes Hetty Rohayani; Sitti Nur Alam; Muhammad Fauzi; Rico Rico
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2577

Abstract

The Covid-19 pandemic in 2020 that occurred in Indonesia is a complex health problem and requires fast handling and collaborative solutions from various disciplines. Covid-19 patients who receive treatment in hospitals have different conditions and severity. This affects the treatment actions that will be carried out by medical officers. The large number of patients and the lack of medical personnel have resulted in the need for technological support to help classify patient status based on their condition so that treatment is concentrated on patients who are very critical and require rapid treatment. The number of employees at the Jambi District Attorney's Office were exposed to the Covid-19 virus in particular, and it is necessary to predict PCR results. This study applies a prediction technique from the data mining discipline section to classify the status of employees at the Jambi District Attorney's Office who are confirmed to be Covid-19. Classifiers using the Naive Bayes method were applied to build a model based on a dataset of patients infected with Covid-19. The Covid-19 PCR result dataset was obtained from the Jambi District Attorney and was applied using RapidMiner. The model built can predict the confirmed status of Covid-19 based on age, gender, health condition, and vaccinations. The results of this study indicate that the classification of the Naive Bayes method has a high level of accuracy in classifying the confirmed status of Covid-19, namely 97%.
Co-Authors Hetty Rohayani Rico Rico Sitti Nur Alam