Comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 method in weather forecast

Abstract

Weather forecast in an area is unpredictable. This is due to the fact that human factors cannot predict it. The weather forecast is by applying data mining using the algorithm Naive Bayes, K-nearest Neighbor (K-NN), and C.45. Bayesian Classification is a statistical classification method that is useful for the process of determining the probability of a class membership. KNN Algorithm is a classification algorithm based on the similarity between one data and another data. C4.5 algorithms is an easy-to-use classification method interpreted. The best level of accuracy between the three algorithms can be determined by comparison. Comparison of algorithm aims to get the algorithm that is considered accurate, precision, recall and f-measure to make a prediction of a problem. the results of the comparison of the k-Nearest Neighbor, Naïve Bayes and C4.5 classification algorithms used in weather prediction case studies stating that the KNN classification algorithm is a classification algorithm that has the highest accuracy with k = 7 and fold = 5 in predicting the weather compared to Naïve Bayes classification algorithm with fold = 3 and C.45 which reached 71.58% followed by C.45 with fold = 20 having an accuracy of 69.83%. and finally Naïve Bayes 68.77%.