Laptop Price Prediction Based on Technical Specifications Using the Random Forest Algorithm - Nabillah Nasywa Syahrani; Satria Nouval Wahyudi; Antonious Ariel Afendra; Wisti Dwi Septiani; Syaifur Rahmatullah Abdul (Sean Institute, 2026)
This study develops a laptop price prediction model based on technical specifications using the Random Forest Regressor algorithm. The dataset, obtained from publicly available platforms such as Kaggle, comprises several key attributes, including brand, device category, processor type, user rating, and price. The analytical procedure involves data preprocessing, categorical feature encoding, model training, and performance evaluation. The evaluation results demonstrate strong predictive performance, with an R² value of 0.938, an RMSE of 22,021, and an MAE of 12,667, indicating high prediction accuracy and the model’s ability to explain more than 93% of the variance in laptop prices. These findings suggest that the Random Forest algorithm is highly effective for developing specification-based laptop pricing models and shows substantial potential for implementation in e-commerce platforms and automated pricing recommendation systems.