Implementasi Data Mining untuk Prediksi Status Proses Persalinan pada Ibu Hamil Menggunakan Algoritma Naive Bayes - Pusadan, Mohammad Yazdi; Ghifari, Ari; Anshori, Yusuf (Pandawan Incorporation, Alphabet Incubator Universitas Raharja, 2023)
Childbirth is the process of taking out the fetus after 20 weeks of gestation or more to be able to live outside the uterus through the birth canal or another way, with or without assistance. Maternal Mortality Rate in Indonesia is still quite high based on the White Book of National Health System Reform in March 2022, at 305 for every 100.000 births. Causes of the high Maternal Mortality Rate is the risky of childbirth process for the mother and the baby. Clinical prediction is growing by adopting computer sience and information technology in data processing, accompanied by data mining methods for processing. The problem of pregnant mother can be anticipated by using the system for predicting the status of the childbirth process with the implementation of data mining and Naïve Bayes algorithm, with the purpose for helping to reduce Maternal Mortality Rate, especially caused by risky childbirth process. This study using 600 training data, then tested using the Confusion Matrix method on 100 testing data. Obtained Precision value was 82.4%, Recall value was 94%, F-Measure value was 88.7 and Accuracy value was 92%.