Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes - Liliana, Dewi Yanti; Maulana, Hata; Setiawan, Agus (MULTINETICS, 2021)
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 www.kaggle.com 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.