KLASIFIKASI TINGKAT KERENTANAN MALARIA PADA SUATU WILAYAH MENGGUNAKAN NAÃVE BAYES DATA MINING

Abstract

The number of people affected by malaria often increases along with climate
change which encourages the growth of vectors (vector borne disease) as an
object that transmits the disease. The quality of the body's low condition will be
vulnerable resulting in the spread of diseases transmitted by insects and animals.
Global commitment regarding malaria elimination which began in 2007 was
based on the high infant, under-five and pregnant mortality rates. One thing that
needs to be done is the need for a classification of the vulnerability level of each
region against malaria from the quantitative data that has been obtained so that it
will provide more emphasis on malaria, especially in areas with higher levels of
vulnerability. Calculation of the classification of the vulnerability level of the region
against malaria using Naive Bayes was able to produce an accuracy value of
93.75%.
Keywords: Classification, Vulnerability, Malaria, Naïve Bayes