Prediksi Penyebaran Virus COVID-19 Dari Hasil PCR Menggunakan Metode Naïve Bayes

Hetty Rohayani; Sitti Nur Alam; Muhammad Fauzi; Rico Rico

doi:10.47065/josyc.v4i1.2577

Hetty Rohayani * Universitas Muhammadiyah Jambi, Jambi, Indonesia
Sitti Nur Alam Universitas Yapis Papua, Jayapura, Indonesia
Muhammad Fauzi Universitas Muhammadiyah Jambi, Jambi, Indonesia
Rico Rico Universitas Adiwangsa Jambi, Jambi, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josyc.v4i1.2577

Keywords: Prediction; Data Mining; Covid-19; Naive Bayes Algorithm; Rapidminer

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%.

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