Stacked Generalization with Sequential-Model Based Optimization for estimating Used Car Valuation in Indonesia

In Indonesia, the purchase and sale of used vehicles is a common practice. The valuation of a used vehicle is influenced by several factors, making it challenging to determine an appropriate selling price. To address this issue, this study employs a stacked generalization (stacking) algorithm to integrate Machine Learning (ML) techniques that have demonstrated efficacy in prior research on used car valuations. The Sequential Model-Based Optimization (SMBO) algorithm is employed to achieve high accuracy while ensuring an efficient hyperparameter optimization process. The initial price of a vehicle is undoubtedly a significant determinant of its resale value. However, this fact is frequently overlooked in previous studies on developing car price estimation models. This study makes a contribution to the field by addressing this issue. The use of the initial price as an input for the model enables two distinct types of analysis: one for the assessment of used car prices and the other for the measurement of the degree of residual valuation of used cars in relation to their initial costs. The results demonstrated that the optimized stacking model exhibited superior predictive ability compared to the other algorithms in both analyses. Feature analysis substantiated the considerable influence of the initial price on the used car's price. This study also corroborates the assertion that accurately predicting the valuation of a used car cannot be achieved by solely considering the usage of the previous owner, such as the car's age and mileage. It is crucial to take into account the car's original attributes, particularly its initial price.