<![CDATA[The complexity of hardware specifications in laptops often makes it difficult for consumers to estimate price suitability in the digital market. This study aims to implement a laptop price classification system by comparing the performance of the baseline Decision Tree algorithm and the Ensemble Learning method (Random Forest) utilizing the Knowledge Discovery in Databases (KDD) approach. Contrary to the initial hypothesis that the ensemble model would provide significant performance improvements, the evaluation results revealed a paradoxical finding. Both classification models produced identically exact performance with an accuracy rate of 73.39% and an F1-Score of 73.41%. Technical analysis indicates that this anomaly is caused by the narrow dimension of deterministic features in the dataset, where RAM capacity and CPU architecture attributes dictate the decision boundaries absolutely, rendering the addition of hundreds of decision trees in the Random Forest computationally redundant. The results of this study contribute theoretical insights regarding the efficiency limitations of ensemble algorithms on low-dimensional datasets, while proving that a single Decision Tree is optimal and computationally efficient enough to be implemented as an inference engine in laptop price recommendation systems.]]>
Copyrights © 2026
