Online Decision Support System of Used Car SelectionUsing K-Nearest Neighbor Technique
Sasitorn Kaewman · International Journal of Future Computer and Communication · 2012
This paper, we present Decision Support Systems (DSS) for used car selection using k-nearest neighbor (k-NN) algorithm. This system presents a user-friendly web-based spatial decision support system aimed for selling vehicle. There are two functions that are searching using k-nearest neighbor algorithm, and searching in the database to find one or more matching datasets with the user query. For a web search query, records contain vehicle type, brand, model, year, size, prince and type of engine. By contrast, k-nearest neighbor was estimated from data normalized of the training data set. The result showed that (DSS) present highly effective and sustainable tools for searching vehicle. Before buying a car, user must get some information or study about what kind of car is suitable for them. Basically, the buyer who wants to buy a new car model can visit the showroom to get more information. In used car, some model is very old and out of the showroom. Moreover, nowadays, internet can be used to get the information. The web database is finding one or more matching datasets with the user query. In this system, user can query the records that are able to select the suitable car. By contrast, the Decision support system (DSS) is the method that will make decision according to the information or feed back that are key in by users.