Klasifikasi Penderita ISPA Menggunakan Metode Naive Bayes Classifier - Syarah, May Siti; Wati, Masna; Puspitasari, Novianti (Department of Informatics, Siliwangi University, Indonesia, 2022)
Information related to the classification of ARI disease suffered by the community in a public health is essential. This is because the public health is one of the community health centers that is a reference for treatment for the community. Public health must identify the right type of ARI disease so that treatment for ARI sufferers can be given optimally. This study classified the data of patients with ARI in a public health based on the determining factors, namely the disease suffered, age, and period of stay. Classification is carried out using the Naive Bayes Classifier method with the Confusion Matrix testing method. The results of applying the Naive Bayes Classifier method to patient data resulted in three types of ARI, namely mild, moderate and severe. The highest number of ARI patients is severe ARI. The results of the Confusion Matrix test that have been carried out prove that this method has an accuracy of 93.33% so it is suitable for use to classify ARI diseases.