Klasifikasi Penentuan Tingkat Penyakit Demam Berdarah dengan menggunakan Algoritma Naïve Bayes (Studi Kasus Puskesmas Nagreg) - Saeful Anwar; Revita Lestari Faujiah; Tuti Hartati; Edi Tohidi (Universitas Wahid Hasyim, 2024)
The rapid development of science and technology, especially in the field of information technology, can give rise to new innovations for presenting and managing information to meet information needs. The role of technology in the health and medical fields has helped a lot in helping the human spirit and has shown its importance. Dengue Hemorrhagic Fever (DHF) is a disease that occurs in children and adults with the main symptoms of fever, muscle and joint pain, which usually gets worse after the first two days. DHF is a public health problem in Indonesia where the number of sufferers tends to increase and its spread causes bleeding. Dengue fever is characterized by sudden high fever lasting 2-7 days without a clear cause accompanied by manifestations such as petechiae, epistaxis sometimes accompanied by vomiting of blood, diarrhea, decreased consciousness, tendency to cause shock and death. The Naïve Bayes algorithm is a form of data classification using probability and statistical methods. The algorithm uses Bayes' theorem and assumes that all attributes are independent or not interdependent given the values of the class variables. Another definition says that Naïve Bayes is a classification using probability and statistical methods discovered by the British scientist Thomas Bayes, namely predicting future opportunities based on previous experience. Proceeding to the final stage, the final stage or step is to see the level of accuracy or how well the classification of the model we are using is.