Analisis Perbandingan Metode Artificial Neural Network dan XGBoost untuk Prediksi Profit dari Data Transaksi Point of Sale - Kurniawan, Panji; Setyaningsih, Putry Wahyu (Building of Informatics, Technology and Science (BITS), 2025)
In the business world, profit is a key indicator of a company’s success, and predicting future profit is essential for strategic decision-making, such as inventory planning, pricing strategies, and marketing efforts. However, market fluctuations and dynamic consumer behavior often make profit prediction a significant challenge. With technological advancements, data mining methods have become increasingly utilized for analyzing such complex datasets, including Artificial Neural Networks (ANN) and XGBoost. This study explicitly aims to compare the performance of ANN and XGBoost in predicting profit based on transactional data from a Point of Sale (POS) system. ANN was selected for its ability to learn intricate and non-linear patterns in data, while XGBoost is known for its efficiency in processing large datasets and preventing overfitting through boosting and regularization techniques. The dataset consists of 44,348 transactions, with 80% used for training and 20% for testing. Results show that the ANN model achieved an R² of 0.9996 and a MAE of 1,359, outperforming the XGBoost model, which obtained an R² of 0.9978 and a MAE of 1,600. This significant difference indicates that ANN delivers more accurate predictions. ANN’s advantage lies in its capacity to develop complex internal representations of data, making it more responsive to subtle patterns in transactional behavior. These findings highlight the importance of choosing the appropriate model for profit prediction and demonstrate that ANN provides superior predictive accuracy, supporting more precise and data-driven strategic decisions for financial and sales management..