Data Mining Klasifikasi Penjualan Motor Menggunakan Kombinasi Algoritma K-Means Dan Naïve Bayes
Abstract
The showroom, which has been established since 2006, which is located in Lapai, Padang city, has problems, namely the difficulty of analyzing consumer demand and a lot of accumulated sales data. In addition, there are many stocks of goods that are not available when consumer demand is high. From these problems a data mining application system is needed to improve sales patterns and process sales data to determine what is often purchased and not by using the data mining method, namely K-Means and Naïve Bayes. The data is obtained directly from CV. Unique Motor in the form of motorcycle sales data and motorcycle inventory data. At the system analysis stage, system design will be carried out using data mining using the K-Means and Naive Bayes algorithms. Where the program will be executed in the PHP and MySQL programming languages. The existence of a classification data mining system using a combination of K-Means and Naive Bayes can speed up the showroom in making decisions from the data taken so that the showroom can increase the number of stocks that have a hot-selling classification, so that the showroom not out of stock. A data mining system designed using a combination of KMeans and Naïve Bayes can assist showrooms in classifying motorcycle sales, as well as being able to align the availability and inventory of existing motorcycles by classifying sales volumes.
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