KLASIFIKASI TINGKAT KERENTANAN MALARIA PADA SUATU WILAYAH MENGGUNAKAN NAÃVE BAYES DATA MINING - Aries Setiawan; Adi Prihandono (VISIKES: Jurnal Kesehatan Masyarakat, 2019)
The number of people affected by malaria often increases along with climatechange which encourages the growth of vectors (vector borne disease) as anobject that transmits the disease. The quality of the body's low condition will bevulnerable resulting in the spread of diseases transmitted by insects and animals.Global commitment regarding malaria elimination which began in 2007 wasbased on the high infant, under-five and pregnant mortality rates. One thing thatneeds to be done is the need for a classification of the vulnerability level of eachregion against malaria from the quantitative data that has been obtained so that itwill provide more emphasis on malaria, especially in areas with higher levels ofvulnerability. Calculation of the classification of the vulnerability level of the regionagainst malaria using Naive Bayes was able to produce an accuracy value of93.75%.Keywords: Classification, Vulnerability, Malaria, Naïve Bayes