COMPARISON OF K-NN AND NAÏVE BAYES CLASSIFIER FOR ASPHYXIA FACTOR - Finki Dona Marleny; Mambang (Fakultas Teknik Universitas Lambung Mangkurat, 2018)
Asphyxia is influenced by several factors, including the factors affecting the Immediate Was maternal factors That relates Conditions mother Pregnancy and childbirth such as hypoxia mother, Asphyxia factor data can be modeled using the classification approach. this paper will be compared k-nearest neighbor algorithm and Naive Bayes classifier to classify asphyxia factor. Naive Bayes uses the concept of Bayes’ Theorem which assuming the independency between predictors. Basically, Bayes theorem is used to compute the subsequent probabilities. Analysis of the two algorithms has been done on several parameters such as Kappa statistics, classification error, precision, recall, F-measure and AUC. We achieved the best classification accuracy with KNN algorithm, 92,27%, for k=4. are lower than the rates achieved with Naïve Bayes 83,19%.