Ev7 Solo Electric Bicycle Sales Recommendation System Using Knowledge Based Method

Abstract

One outlet that offers various types of electric bicycles is EV7 Solo. EV7 Solo is a large shop that sells electric bicycles of various brands and types. The services provided by the team at the shop currently still work using traditional methods, where customers need to come directly to the shop to choose and buy products. The density of potential buyers coming at the same time makes the service from EV7 Solo slow and makes potential buyers feel uncomfortable. Therefore, it is necessary to develop a recommendation system that can help customers choose products. electric bicycle. This research aims to design an Electric Bicycle Product Selection Recommendation System using knowledge-based methods. The system development method used in this research is Rapid Application Development (RAD), which includes the stages of business modeling, data modeling and process modeling. Knowledge-based recommendation systems have the advantage of prioritizing user needs for products by calculating the similarities between customer needs and electric bicycle product attributes. This knowledge-based electric bicycle product selection recommendation system model provides five search attributes for electric bicycle products, namely brand, price, mileage, color and maximum speed. By using 30 sample data, the results of the knowledge-based recommendation method modeling can provide recommendations for electric bicycle products based on criteria set by customers through calculating the similarity between customer needs and the attributes listed on the electric bicycle product. Product Data: Electric bicycle products with the highest similarity value will be recommended to customers, such as the Powelldd a400 Black electric bicycle product which received the highest similarity value of 0.795. The results of this research can serve as a guide for developing a recommendation system in selecting electric bicycle products.