Predicting the Selling Price of Cars Using Business Intelligence with the Feed-forward Backpropagation Algorithms - Idris, Nur Oktavin; Achban, Aspian; Utiarahman, Siti Andini; Karim, Jorry; Pontoiyo, Fuad (2020 Fifth International Conference on Informatics and Computing (ICIC), 2020)
The automotive industry is increasingly competitive every year by releasing cars featured with innovative specifications offered by automotive manufacturing companies. The specifications, supported by the technology and performance a car has, are a tool to determine a car's price. However, today the automotive industry frequently releases a new product or type of car with the latest specifications, affecting a car's price to change. It perplexes car manufacturing companies when they are determining a car's price. Responding to this issue, an approach to a decision-making strategy to predict a car's price is needed. One of the approaches that can be implemented is business intelligence with its primary aspects i.e. descriptive, predictive, and prescriptive. Using the concept, we implement Business Intelligence and use the feed-forward backpropagation algorithm to predicts the selling price of a car based on its specification and predict a car price based on the latest specification which has never been on sale. The research findings, identified by using a dataset containing the specifications of BMW, reveal that the actual price and predicted price are close at a mean error of 11.46%. Besides, the research findings also state that the predicted price of a new car with new specifications is 55,754. This research aims to analyze the estimation of the price of a car with the latest specification, which is the focus of the implementation of the business intelligence method we do.