Forecasting of Dengue Hemorrhagic Fever and Climate Influences Using the Linear Regression Model at Tabanan Hospital
DOI:
https://doi.org/10.53713/htechj.v1i4.80Keywords:
forecasting, haemoragic fever, regresi linierAbstract
Forecasting is the science of predicting future events using final data. DHF is an infectious disease caused by mosquitoes carrying the dengue virus. Based on data from the Ministry of Health for 2020, there were 95,893 cases, with 661 cases dying. This study aimed to determine the number of patient cases in the future and the influence of climate factors on DHF in Tabanan Hospital. Prediction analysis in this study using the Linear regression method. Evaluation of measurement measurements using MAPE and significance tests, namely the F test and t-test to determine the magnitude of the influence of the independent variables on the dependent variable. This study resulted in a prediction of DHF at Tabanan Hospital in the 65th period of May 2022, totaling 74 DHF cases with an accuracy of the MAPE forecasting error rate of 16.99% in the good category. The F test shows a significance value of 0.31, more significant than the value of α (0.05), and the variable’s rainfall, air temperature, and air humidity do not significantly affect DHF cases. The results of the regression test with the t-test showed that the t-count Rainfall is -1,615 with a significance value of 0.11 > 0.05 where the t-count is a negative value which means that if rainfall increases, then the risk of DHF will decrease, the t value counts Temperature air is 0.278 with a significance value of 0.78 > 0.05, the t value for air humidity is 1.845 with a significance value of 0.07 > 0.05. With accurate prediction results, it is hoped that Tabanan Hospital management can improve hospital services and services.
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