DENGUE FEVER CASE PREDICTION MODEL USING LINEAR REGRESSION WITH EXPLANATORY SEQUENTIAL MIXED METHODS APPROACH

Conchita Junita Chandra; Yoseph Thobias Pareira

doi:10.34288/jri.v8i2.484

Authors

Conchita Junita Chandra Program Studi Teknik Informatika, Universitas Nusa Nipa
Yoseph Thobias Pareira Program Studi Arsitektur, Universitas Nusa Nipa

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i2.484

Keywords:

Prediction, DHF, Building area, total popoulation, linear regression

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease in Indonesia, including in Sikka Regency, where the number of cases has increased over the past decade. Predicting the number of DHF cases is crucial to support disease prevention and control policies. This study aims to develop a predictive model for the number of dengue fever cases based on building area, population, and population density, moreover to explain other factors that influence the prediction results. The study uses an explanatory sequential mixed methods approach, and the prediction model is developed using simple linear regression and multiple linear regression. Quantitative data were obtained from the Health Office, the Sikka Regency Statistics Office, and Google Earth; while qualitative data were obtained through interviews with surveillance personnel from the Health Office and several community health centers in the study area, using a purposive sampling technique. The results show that the building area has a weak relationship with the number of DHF cases (R² = 0.10334 for Alok Timur sub-district and R²=0.38055 for Waiblama). After adding the population and population density variables, the R² in Alok Timur increases to 0.46974; and R²=0.41024 for Waiblama; however, the accuracy is still low. The interviews results show that community behavior is the dominant factors of DHF cases. This study indicates that predictive models based on physical environmental and population variables are unable to accurately depict the complexity of dengue fever case distribution. Therefore, the development of models that integrate community behavioral factors is necessary to provide more accurate predictions.

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