Product Demand Analysis Using the XGBoost Algorithm at PT Atmadjaya Sembada Anugerah

Okky Julian Atmajaya Tarmoko; Rinabi Tanamal

doi:10.24167/sisforma.v13i1.13650

Product Demand Analysis Using the XGBoost Algorithm at PT Atmadjaya Sembada Anugerah

Okky Julian Atmajaya Tarmoko, Rinabi Tanamal

Abstract

PT Atmadjaya Sembada Anugerah is a frozen food manufacturing company that faces challenges in stock management due to unpredictable daily fluctuations in product demand. Inaccurate demand forecasting can lead to inefficiencies in distribution and storage operations. This study aims to apply the Extreme Gradient Boosting (XGBoost) algorithm to forecast product demand using historical daily sales data. The process involves exploratory data analysis, data cleaning, feature engineering for time and statistical variables, and time-based data splitting. The model is trained using features selected through Recursive Feature Elimination and optimized using hyperparameter tuning with Optuna. Evaluation is conducted through TimeSeriesSplit cross-validation and assessed using three standard performance metrics. The results indicate that the model effectively captures seasonal patterns and general demand trends, although it remains limited in responding to sudden demand spikes. These findings support the use of XGBoost as a foundational approach for demand forecasting systems in stock planning, with potential for further improvement through the integration of external data and expanded feature sets.

Keywords

XGBoost; Machine Learning; Demand Forecasting; Time Series; Sales Prediction; Historical Data; Stock Planning

Full Text:

PDF

References

D. Karthika and K. Karthikeyan, “a Recent Review Article on Demand Forecasting,” Xi’an University of Architecture & Technology, vol. 12, no. 3, pp. 5769–5777, 2020.

Y. F. Akande, J. Idowu, A. Misra, S. Misra, O. N. Akande, and R. Ahuja, “Application of XGBoost Algorithm for Sales Forecasting Using Walmart Dataset,” Lecture Notes in Electrical Engineering, vol. 881, no. August, pp. 147–159, 2022, doi: 10.1007/978-981-19-1111-8_13.

M. Mohamed, F. E. Mahmood, M. A. Abd, M. Rezkallah, A. Hamadi, and A. Chandra, “Load Demand Forecasting Using eXtreme Gradient Boosting (XGboost),” 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023, no. December, 2023, doi: 10.1109/IAS54024.2023.10406613.

Muhammad Dzul Asmi Alhamdi, Herman, and Wistiani Astuti, “Peramalan Kebutuhan Obat Menggunakan XGBoost Studi Kasus pada Rumah Sakit XYZ,” Indonesian Journal of Computer Science, vol. 12, no. 5, pp. 2757–2764, 2023, doi: 10.33022/ijcs.v12i5.3344.

R. Tanamal, N. Minoque, T. Wiradinata, Y. Soekamto, and T. Ratih, “House Price Prediction Model Using Random Forest in Surabaya City,” TEM Journal, vol. 12, no. 1, pp. 126–132, 2023, doi: 10.18421/TEM121-17.

I. M. Sukarsa, N. N. Pandika Pinata, N. Kadek Dwi Rusjayanthi, and N. W. Wisswani, “Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost,” TEM Journal, vol. 10, no. 1, pp. 144–151, 2021, doi: 10.18421/TEM101-17.

D. Swami, A. D. Shah, and S. K. B. Ray, “Predicting Future Sales of Retail Products using Machine Learning,” pp. 1–6, 2020, [Online]. Available: http://arxiv.org/abs/2008.07779

A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian Journal on Computer and Information Technology), vol. 5, no. 1, pp. 75–82, 2020, doi: 10.31294/ijcit.v5i1.7951.

S. S. Mahadik, I. Technology, and N. Mumbai, “A New AGE Forecasting Model PROPHET,” no. June, 2023, doi: 10.55041/IJSREM15522.

L. S. Ihzaniah, A. Setiawan, and R. W. N. Wijaya, “Perbandingan Kinerja Metode Regresi K-Nearest Neighbor dan Metode Regresi Linear Berganda pada Data Boston Housing,” Jambura Journal of Probability and Statistics, vol. 4, no. 1, pp. 17–29, 2023, doi: 10.34312/jjps.v4i1.18948.

J. Rombouts, M. Ternes, and I. Wilms, “Cross-temporal forecast reconciliation at digital platforms with machine learning,” Int J Forecast, vol. 41, no. 1, pp. 321–344, 2024, doi: 10.1016/j.ijforecast.2024.05.008.

A. M. Priyatno and T. Widiyaningtyas, “a Systematic Literature Review: Recursive Feature Elimination Algorithms,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 9, no. 2, pp. 196–207, 2024, doi: 10.33480/jitk.v9i2.5015.

V. Sinap, “Improving Machine Failure Prediction with Grey Wolf, Whale Optimization, and Optuna Techniques,” Gazi University Journal of Science Part A Engineering and Innovation, vol. 12, pp. 154–174, Mar. 2025, doi: 10.54287/gujsa.1544942.

DOI: https://doi.org/10.24167/sisforma.v13i1.13650