Finding The Most Desirable Car Using K-Nearest Neighbor From E-Commerce Websites

Naufal, Mohammad Farid and Wibisono, Yudistira Rahadian (2021) Finding The Most Desirable Car Using K-Nearest Neighbor From E-Commerce Websites. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer (ELTIKOM), 5 (1). pp. 25-31. ISSN 2598-3245 (Print), ISSN 2598-3288 (Online)

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The increasing number of cars that have been released to the market makes it more difficult for buyer to choose the choice of car that fits with their desired criteria such as transmission, number of kilometers, fuel type, and the year the car was made. The method that is suitable in determining the criteria desired by the community is the K- Nearest Neighbors (KNN). This method is used to find the lowest distance from each data in a car with the criteria desired by the buyer. Euclidean, Manhattan, and Minkowski distance are used for measuring the distance. For supporting the selection of cars, we need an automatic data col-lection method by using web crawling in which the system can retrieve car data from several ecommerce websites. With the construction of the car search system, the system can help the buyer in choosing a car and Euclidean distance has the best accuracy of 94.40%.

Item Type: Article
Uncontrolled Keywords: E-Commerce,Euclidean Distance, K Nearest Neighbors, Manhattan Distance, Minkowski Distance
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Engineering > Department of Informatic
Date Deposited: 22 Jun 2021 02:20
Last Modified: 22 Jun 2021 02:20

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