Implementation of Naïve Bayes Classification Method for Predicting Purchase

To choose the right vehicle according to the needs and funds owned by consumers, requires a careful analysis that takes into account many criteria and factors. The criteria used as a benchmark in choosing a vehicle, among others, price, spare parts, cylinder volume, the power of the vehicle. To process all these criteria required a system that can select and classify criteria chosen by consumer, so that can assist consumer in choosing the most appropriate vehicle, therefore needed a system for decision making in making car purchase. The Naive Bayes algorithm is a simple probabilistic classifier that computes a set of probabilities by summing the frequency and value combinations of the given dataset. Application of Naïve Bayes method is expected to be able to predict car purchases. Of the 20 car purchase data used in the test by the Naïve Bayes method, then obtained a percentage of 75% for the accuracy of prediction, where from 20 car purchase data tested there are 15 data purchase car successfully classified correctly.