PENENTUAN TINGKAT PENJUALAN MOBIL DI INDONESIA DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES

Ikhsan Romli, Esem Pusnawati, Arif Siswandi

Abstract


Cars are one of the vehicles most often found in various types and brands. Cars have various specifications. The Naive Bayes method is one of the classification and branching methods of artificial intelligence. The various brands will be in the form of a Class that are Laris and Not Selling, so that consumers, producers, and researchers can find out which car brands are best selling based on their category and output. Naive bayes is a widely used classification method because of its simple and high accuracy in classifying data. This study analyzed data as many as 639 data into 511 training data and 128 testing data, data was obtained from the Indonesian Automotive Industries Association (GAIKINDO) site. With attribute 19, to facilitate the writer in the study the attributes used were 8 (including 1 Class attribute that author added to facilitate the search for the best-selling car). The results of the conducted research gave the classification of car brand differentiators that were most in demand by consumers and the best-selling categories. The level of classification accuracy with the Naive Bayes Method produces accuracy values of 92, 19%, Precision values: 98, 39% and Recall values: 87, 14% so that the Naive Bayes Method is a pretty good method in this research.

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