Abstract
Dengue fever has been declared endemic in many cities of Indonesia, one of them being the Malang City. In 2015, the incidence of dengue fever in the region was recorded at 1,629 with 13 deaths. There are many factors that contribute to the disease. The factors associated with dengue-fever transmission include population density, population mobility, quality of housing and attitude of life. However, the factors that can trigger dengue fever are environmental in nature, and include changes in temperature, humidity and rainfall, which cause mosquitoes to lay eggs more often and facilitates a rapid reproduction of the dengue virus. Parasites and disease carriers (mosquitoes) are very sensitive to climatic factors, especially temperature, rainfall, humidity, water levels and wind. Therefore, this study aimed to develop a suitable model for forecasting dengue fever in Malang City based on the Transfer Function and Artificial Neural Network (ANN). Data used were dengue fever data from 2004 to 2019. The results showed that the smallest RMSE, MAPE and SMAPE values of the two models were ANN models.
 Keywords: Artificial Neural Network (ANN), transfer function, dengue fever
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