Classification Prediction of Dengue Fever Spread Using Decision Tree with Time-Based Feature Expansion - Hawa, Iqlima Putri; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant (International Journal on Information and Communication Technology (IJoICT), 2024)
In Indonesia, dengue hemorrhagic fever (DHF) has become a serious community health concern due to fluctuating incidence rates influenced by several factors. It requires comprehensive control strategies to prevent the rise of the incidence. This study seeks to classify the future spread of DHF in Bandung City, accompanied by optimal factors that influence the increase in its spread. This study proposes using Decision Tree to predict a classification of dengue hemorrhagic fever (DHF) spread with implementation of spatial time-based feature expansion. The developed scenario is to build a target class classification prediction model based on the previous time period. From the developed scenario, the selected model has optimal performance to form a classification prediction model in the future. The results obtained show that the performance of Decision Tree using time-based feature expansion is more than 90%. The contribution of this study is to inform the public and health institution regarding DHF spread for the future and influential factor so that the government can provide policies as early as possible to prevent DHF spread.