@article{ART003153519},
author={Gwangwon Jung and Keewon Kim},
title={Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={12},
pages={149-157}
TY - JOUR
AU - Gwangwon Jung
AU - Keewon Kim
TI - Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 12
PB - The Korean Society Of Computer And Information
SP - 149
EP - 157
SN - 1598-849X
AB - Accurate meal attendance prediction positively impacts cafeteria operations by reducing food waste and lowering costs. In the past, predictions were based on human experience, but modern methods involve developing predictive models. These models offer advantages such as reduced time and resource consumption compared to traditional methods. However, practical implementation remains limited due to various challenges. This paper utilizes cafeteria data from 2018, 2019, and 2023 semesters, along with weather data from the Korea Meteorological Administration, to select variables through EDA analysis.
Using these variables and machine learning algorithms, models were developed to predict breakfast, lunch, and dinner attendance. The best-performing model was selected as the final model to develop a prediction program. 80% of the data was used for training and 20% for validation. The final models selected were XGBoost for breakfast (MAE ratio 12.97%) and RandomForest for lunch and dinner (MAE ratios 4.8% and 4.93%, respectively). The lunch and dinner prediction models demonstrated good performance. Future work will involve continuous data collection, adding new derived variables, hyperparameter tuning, and UI improvements to enhance model performance and convenience.
KW - Cafeteria;Predictive Model;EDA;Machine Learning;Deep Learning
DO -
UR -
ER -
Gwangwon Jung and Keewon Kim. (2024). Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance. Journal of The Korea Society of Computer and Information, 29(12), 149-157.
Gwangwon Jung and Keewon Kim. 2024, "Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance", Journal of The Korea Society of Computer and Information, vol.29, no.12 pp.149-157.
Gwangwon Jung, Keewon Kim "Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance" Journal of The Korea Society of Computer and Information 29.12 pp.149-157 (2024) : 149.
Gwangwon Jung, Keewon Kim. Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance. 2024; 29(12), 149-157.
Gwangwon Jung and Keewon Kim. "Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance" Journal of The Korea Society of Computer and Information 29, no.12 (2024) : 149-157.
Gwangwon Jung; Keewon Kim. Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance. Journal of The Korea Society of Computer and Information, 29(12), 149-157.
Gwangwon Jung; Keewon Kim. Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance. Journal of The Korea Society of Computer and Information. 2024; 29(12) 149-157.
Gwangwon Jung, Keewon Kim. Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance. 2024; 29(12), 149-157.
Gwangwon Jung and Keewon Kim. "Development of Machine Learning-Based Predictive Models and a Web App for University Cafeteria Attendance" Journal of The Korea Society of Computer and Information 29, no.12 (2024) : 149-157.
