Perbandingan Algoritma Naive Bayes dan Linear Discriminant Analysis dengan Dataset Car Evaluation

Abstract

Safety, price, and luxury are important factors to consider when buying a car. These factors depend on the type, model and make of the vehicle. In fact, these factors are very important in terms of reducing the number of accidents. However, the many variables that must be considered make it difficult for consumers to determine which car to buy and are prone to human error. With these problems, the need for an efficient decision-making system, one of which uses Machine Learning algorithms, the author tries to find out the difference between the Naïve Bayes method and Linear Discriminant Analysis (LDA) on the Car Evaluation data set. It is hoped that from this research, the accuracy of the two methods on the Car Evaluation data set can be known. the results of the tests that we carried out using both classifications obtained the results of 73.98% and 82.23% for the time range of 0.002 and 0.001.