Comparison of NB and NB-PSO to determine level of vehicles sales

Abstract

Nowadays, there are various types and brands of vehicles in Indonesia, especially kinds of motor vehicles. In this case, motor vehicles are cars, trucks, and buses (exclude the motorcycles). This research classified the various brands of motor vehicles in the form of classes that are Well Selling (Laris) and Not Selling (Tidak Laris), so that consumers and producers can find out which motor vehicles brands are well selling based on their category and output. This study analyzed 3908 data into 3126 training data and 782 testing data. The data was obtained from the GAIKINDO (Indonesian Automotive Industries Association) site. There were 19 attributes but to ease the research, the attributes used are 8 (including 1 Class attribute to facilitate the search for the best-selling motor vehicles). This research compared the accuracy value among Naive Bayes method and NB-PSO (Naive Bayes- Particle Swarm Optimization) by using such dataset. NB-PSO is adjusted parameters of 1 inertia weight and 5 population size. The results of classification accuracy with the Naive Bayes method produces accuracy values of 92.11%, Precision values: 86.57% and Recall values: 97.12%. Meanwhile, the solutions of NB-PSO have accuracy values of 92.44%, Precision values: 87.07% and Recall values: 97.18%, so PSO method was able to improve the accuracy of classification of NB as many as 0.33%.