Implementation of Classification Algorithm C4.5 in Determining the Emergency Patient in the Maternity Hospital Queue System

Abstract

Based on the epidemiological update or weekly spread of Covid-19 on 23 February 2021, Indonesia was ranked second in the Southeast Asia region in the highest new case reporting. The Indonesian government has taken various countermeasures to suppress the spread of Covid-19, starting from implementing health protocols for the public in public places to the Enforcement of Restrictions on Community Activities. Health facilities are shared facilities included in the essential sector, allowing them to operate 100% by regulating operating hours and capacity and implementing more stringent health protocols. This study aimed to implement a patient classification model using the C4.5 algorithm in determining emergency patients in the maternity hospital queue system. The methodology used in this study is the Cross-Industry Standard Process for Data Mining (CRISP-DM). In contrast, the evaluation of classification data uses the Confusion Matrix and Receiver Operating Characteristics (ROC). The implementation of the C4.5 algorithm in the Maternity Hospital Queue System is used to classify emergency and nonemergency patients. The classification accuracy level obtained in this study was 97.08%, and the AUC value received was 0.984.