Penentuan Jenis Persalinan Berdasarkan Faktor-Faktor Klinis Menggunakan K-NN dan Naive Bayes - Ibrahim, Kristianto Oggie; Candrasari, Diwahana Mutiara; Hariningsih, Rosa Ratri Kusuma (Universitas Ivet, 2024)
The purpose of this research is to classify the type of normal or caesarean delivery using the K-NN and Naïve Bayes algorithms. The data used are data on maternal age, hemoglobin, gestational age, pregnancy problems. The results of this study show that the Naïve Bayes algorithm is able to classify the type of normal or cesarean delivery. With the validation method K-Fold Cross Validation and Confusion Matrix for the calculation of misclassification which will make us know how bad our model is in making predictions, to calculate the accuracy level of the classification method we use. The results show that in the calculation of Cross Validation validation, the accuracy presentation value is 63.57% for training data and 95.48% for testing data and in Confusion Matrix, the accuracy presentation value is 66.2% for training data and 96.18% for testing data.