Algoritma Klasifikasi Naive Bayes dalam Identifikasi Pasien Demam Berdarah Dangue (DBD) - Moch. Anjas Aprihartha; Zulhandi Putrawan; Dicky Zulhan (Upgrade : Jurnal Pendidikan Teknologi Informasi, 2025)
Dengue fever (DF) is an illness caused by the Dengue virus, transmitted to humans through the bite of female Aedes aegypti mosquitoes, and the rise in DF cases often leads to a surge in hospital visits that can result in shortages of beds and medical personnel. In severe conditions, patients require treatment from health professionals experienced in managing this disease, and with advancements in scientific methods, classification techniques have become essential in identifying the severity level of DF patients to determine immediate and necessary treatment. This study aims to classify DF patients who require inpatient care by applying the Naive Bayes method to 230 observation records obtained from medical data of DF patients at Anwar Makkatutu Hospital in Bantaeng Regency during the 2019–2020 period, with model performance evaluated using a confusion matrix. The findings show that the Naive Bayes algorithm demonstrates fairly good performance in identifying patients who need hospitalization and those who do not, indicated by its AUC, accuracy, sensitivity, and specificity values of 0.702, 70.11%, 59.09%, and 81.40%, respectively. These results support more efficient allocation of limited healthcare resources and offer practical implications for clustering DF patients who require medical attention, enabling health authorities to improve planning, prepare adequate medical facilities, and optimize treatment readiness, while also contributing valuable insights to the scientific literature on related topics.