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All Journal G-Tech : Jurnal Teknologi Terapan
Novianto, Bagas Dwi
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Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Analysis of Selling Price Determination With Gradient Boosting Algorithm in Traditional Market Stores Novianto, Bagas Dwi; Kusrini , Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7264

Abstract

Traditional market traders often face difficulties in determining optimal selling prices amid competition from modern retailers. This study aims to apply and compare Gradient Boosting and XGBoost algorithms to develop a selling price prediction model for traditional market stores. The research utilizes two datasets : a large-scale dataset from annual sales data and a small-scale dataset from one month of sales. Model training involves hyperparameter optimization using GridSearchCV and evaluation through metrics such as RMSE, MAE, R², and MAPE. Additionally, feature importance and SHAP analyses were conducted to interpret model behavior. The results demonstrate that both models performed well, with R² values nearing 1.0 and MAPE below 2%. Gradient Boosting outperformed XGBoost on the large dataset, while XGBoost showed better accuracy on the small dataset. These findings highlight the potential of machine learning in supporting data-driven pricing strategies for traditional markets.
Co-Authors Kusrini , Kusrini