Analisis Klasifikasi Kelahiran Caesar Menggunakan Algoritma Naive Bayes

Abstract

Maternal mortality rate (MMR) is the number of maternal deaths during pregnancy to postpartum caused by pregnancy, childbirth or its management and is not caused by accident in every 100,000 live births. Caesarean birth is the last alternative in labor, due to high risk factors. Despite the high risk, the Caesar birth rate has increased significantly, especially in Indonesia. WHO set a country's Caesar labor standards ranging from 5-15 percent per thousand births in the world. Machine learning is a way of machine learning from data. There are several machine learning applications that have been developed. One of them is WEKA machine learning. Weka is a data mining software that is equipped with standard machine learning algorithms, including classification,. In this study the classification of Caesarean births was performed using the Naïve Bayes algorithm to determine whether the birth would be given a Caesarean section or a normal delivery. The dataset used consisted of 80 data on pregnant women with 5 attributes, namely: age, number of medical personnel, time of delivery, blood pressure, liver problems. The results of this study are expected to help the world of health, especially to determine the birth process needs to be given a Caesarean or not by providing a classification model for medical predictions. The results of this study are expected to support the world of health, especially in the case of mothers giving birth by providing a classification model that can be used to make predictions and other medical decision-making