Cash Flow Prediction System of PT Gudang Garam Using ERP-Integrated LSTM

Novandi, Muhammad Ananta Arya
Nurhaida, Ida
Sofia, Irma Paramita
Nurhidayah, Fitriyah

Electronic Journal of Education, Social Economics and Technology
Vol 6, No 2 (2025)

Enterprise Resource Planning (ERP) applications such as Odoo generally do not have predictive analytics capabilities for time series data and are limited to recording historical financial data. The limitations of ERP systems make companies dependent on traditional statistical models such as ARIMA, which often fail to capture complex non-linear patterns in financial data. However, the ability to accurately predict cash flow is crucial for strategic financial management in companies. This study aims to develop and evaluate a predictive model using a Long Short-Term Memory (LSTM) approach that is accurate and integrated into Odoo ERP. The research method includes designing a microservices architecture with FastAPI as a bridge between Odoo ERP, the predictive model, and prediction graph visualization. The LSTM model is evaluated by comparing it with the ARIMA model using 3,740 Daily cash flow data, with evaluation metrics MAE, MAPE, R2. System testing will use Black Box Testing and White Box Testing. The research results show that LSTM significantly outperforms the ARIMA model with an R2 evaluation of 0.8801 and an accuracy of 96.62%. The system testing results also yielded positive outcomes as the integration architecture runs stably and functionally. This research contributes by providing an Odoo ERP system that has predictive analysis capabilities with interactive graphical visualizations through Grafana, which helps companies make decisions effectively.