Product Demand Forecasting in E-Commerce with Big Data Analytics: Personalization, Decision Making and Optimization

Authors

  • Cahyasari Kartika Murni Institut Teknologi dan Bisnis Widya Gama Lumajang
  • Achmad Firman Choiri Institut Teknologi dan Bisnis Widya Gama Lumajang
  • Febriane Devi Rahmawati Institut Teknologi dan Bisnis Widya Gama Lumajang

DOI:

https://doi.org/10.30741/jid.v3i2.1548

Keywords:

ARIMA, Big data, Data Analysis, E-commerce, XGBoost

Abstract

This study explores the role of Big Data in forecasting product demand in the e-commerce sector through the application of machine learning and time series methods. A quantitative descriptive approach is used, involving data collection, preprocessing, analysis, and model evaluation. Forecasting techniques applied include ARIMA for time series prediction and XGBoost for supervised learning to identify key demand factors. Model performance is evaluated using accuracy metrics such as RMSE, MAE, and MAPE. The results indicate that the XGBoost model provides the highest forecasting accuracy at 89%, while the ARIMA model achieves 78%. These findings demonstrate that Big Data significantly supports strategic decision-making in e-commerce by enhancing personalization, optimizing inventory, and enabling data-driven marketing strategies.

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Published

2025-04-24

How to Cite

Murni, C. K., Choiri, A. F., & Rahmawati, F. D. (2025). Product Demand Forecasting in E-Commerce with Big Data Analytics: Personalization, Decision Making and Optimization. Journal of Informatics Development, 3(2), 1–6. https://doi.org/10.30741/jid.v3i2.1548

Issue

Section

Articles