Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis - Viola Yuniza; Atus Amadi Putra; Nonong Amalita; Fadhilah Fitri (Departemen Statistika Universitas Negeri Padang, 2024)
Dengue fever is a disease transmitted by the bite of the Aedes aegypti mosquito. Central Agency of Statistic of Lima Puluh Kota District reported that the morbidity rate of this disease was 14.40% per 100,000 population, which was higher than the previous year's morbidity rate of 3.30% per 100,000 population. The main symptoms of this disease are fever lasting 2-7 days, muscle and joint pain with or without rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms ranging from dengue fever, dengue hemorrhagic fever to dengue shock syndrome. Therefore, a classification method is needed to help and facilitate early diagnosis of this disease. The method used is the Naive Bayes algorithm by classifying the positive and negative patients with dengue fever. The purpose of this research is to determine the classification of patients with dengue fever disease and the accuracy of using the Naive Bayes algorithm. The results of the analysis stated that the Naïve Bayes model successfully classified patients into 12 Dengue fever positive patients and 22 Dengue fever negative patients based on 34 testing data. The accuracy of the model is 91,18%, which shows that the model is very good in classifying Dengue fever patients.