THE EFFECT OF WEIGHT OPTIMIZATION USING GENETIC ALGORITHM ON THE CLASSIFICATION OF DHF VULNERABILITY LEVEL

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the Aedes Ageypti mosquito. In South Kalimantan, especially in the city of Banjarbaru, the number of cases tends to increase every year. Existing research has identified the level of dengue susceptibility by using computational methods, one of which is classification. The method used in this research is Neural Network Backpropagation with weight optimization using Genetic Algorithms for data classification of dengue disease in Banjarbaru City. The purpose of this study was to determine the performance of the classification of dengue susceptibility levels using Neural Network Backpropagation and weighting using Genetic Algorithms. The results showed that the performance obtained for the classification of the level of dengue susceptibility using the Neural Network Backpropagation Algorithm was 83.33% in the accuracy, 96.51% precision, and 84.69% recall, whereas when using the Neural Network Backpropagation Algorithm based on Genetic Algorithm for weight optimization, obtained an accuracy value of 96.29%, a precision of 98.97%, and a recall of 97%.