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Wahyudi, Satria Nouval
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Computer Science & IT

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Laptop Price Prediction Based on Technical Specifications Using the Random Forest Algorithm Syahrani, Nabillah Nasywa; Wahyudi, Satria Nouval; Afendra, Antonious Ariel; Septiani, Wisti Dwi; Abdul, Syaifur Rahmatullah
INFOKUM Vol. 14 No. 02 (2026): Infokum, March-April 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v14i02.3066

Abstract

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.
Co-Authors Abdul, Syaifur Rahmatullah Afendra, Antonious Ariel Syahrani, Nabillah Nasywa Wisti Dwi Septiani