PENERAPAN ALGORITMA NAÏVE BAYES UNTUK MEMPREDIKSI GEJALA DEMAM TIFOID PADA PUSKESMAS CIBADAK - Abdullah Khabari Kamil; Resti Yulistria; Apip Supiandi; Gunawan Gunawan (Jurnal Larik: Ladang Artikel Ilmu Komputer, 2021)
Typhoid fever is an infectious disease that is still a health problem in developing countries, especially in Indonesia. Salmonella typhi is a bacterium that causes typhoid fever which can be transmitted through food or drink contaminated by feces or urine from an infected person. The first step in managing typhoid fever is determining the right diagnosis. To reduce detection errors and avoid delays in diagnosis of typhoid fever sufferers, the application and utilization of data mining techniques can be carried out. One of the algorithms that can be applied is Naive Bayes Classifier, with the implementation of the Naive Bayes Classifier algorithm it is expected that sufferers can find out their health condition from typhoid fever that may occur, so they can immediately take action in an effort to minimize the symptoms that occur and are expected to take action early on. this makes other symptoms that will occur just do not occur and the symptoms are reduced. The Naïve Bayes Classifier is a well-known classification model and is often used. The results of this study get an accuracy of 93.71%. using rapid miner 5.2 with 142 datasets.