Klasifikasi Tindakan Persalinan Pada Pasien Ibu Bersalin Menggunakan Metode Decision Tree C4.5
DOI:
https://doi.org/10.30865/klik.v4i1.1168Keywords:
Childbirth; Data Mining; Classification; Decision Tree; C4.5Abstract
Childbirth is the process of delivering a baby, placenta, and amniotic sac from the uterus to the outside world. According to data from the World Health Organization, there are at least 303 thousand women worldwide who die on the verge of or during the childbirth process. Childbirth methods can vary, it could be through normal delivery or cesarean delivery, which are usually based on the health conditions of the mother and baby. Therefore, the selection of the appropriate childbirth method can increase the safety of the mother and baby. Hence, through this research, childbirth methods need to be examined more deeply with the aim of finding out what factors influence them, and then determine the childbirth method based on those factors. In grouping childbirth methods based on childbirth factors, a data mining method is used, namely classification. The Decision Tree C4.5 method is used in this research because of its ability to produce a classification model that is easy to understand and interpret. This model is built based on historical data from Banyuasin Regional General Hospital that includes various health variables and childbirth methods. Testing was conducted using childbirth method data from January 1, 2020 to December 31, 2020. This research produced 20 decision branch patterns or rules that form the basis for determining the label or class data with an accuracy rate of 99.26%.
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