Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IJIIS: International Journal of Informatics and Information Systems
Wawolangi, Ariel Christopher
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Forecasting Coffee Sales Using Time-Based Features and Machine Learning Models Wijaya, Yoana Sonia; Wawolangi, Ariel Christopher
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.294

Abstract

Sales forecasting is a critical component of operational and strategic decision-making in retail and coffee businesses, where demand exhibits strong temporal variability and product-level heterogeneity. Accurate hourly-level forecasts enable effective inventory management, workforce scheduling, and data-driven promotional strategies. However, existing studies predominantly rely on aggregated sales data and provide limited comparative analyses between traditional statistical models and machine learning approaches using real transaction-level data. This study addresses this gap by conducting an empirical comparison between a traditional ARIMA model and ensemble machine learning models, namely Random Forest and XGBoost, for hourly coffee sales forecasting. The analysis is based on a real-world dataset comprising 3,547 transaction records enriched with temporal and product-related features. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results demonstrate that machine learning models significantly outperform the ARIMA baseline, with XGBoost achieving the best performance and explaining approximately 83% of the variance in sales data, while ARIMA shows limited explanatory power due to its inability to capture non-linear and highly volatile demand patterns. Feature importance analysis further reveals that product-specific attributes are the dominant drivers of sales predictions, complemented by seasonal and intra-day temporal effects. These findings provide both scientific and practical contributions by offering empirical evidence on the superiority of machine learning models for granular sales forecasting and supporting data-driven decision-making in coffee retail analytics
Co-Authors Wijaya, Yoana Sonia