Predictive Analysis of Raw Material Stock at Puri Food and Healthy, an SME, Using the Long Short-Term Memory (LSTM) Method

Micro, Small, and Medium Enterprises play a vital role in the Indonesian economy, yet face significant challenges in managing raw material inventories, particularly for perishable commodities such as coconut sap (nira). This study applies and optimizes the Long Short-Term Memory (LSTM) method to predict raw material stock levels for coconut sap at Puri Food and Healthy, an SME, using five years of daily historical data (January 1, 2020–December 31, 2024; 2,191 entries). A descriptive and experimental quantitative approach was employed to develop a deep learning-based predictive model, with data obtained through inventory documentation and interviews with SME managers. The research process encompassed data preparation, collection, normalization, LSTM model construction using Python and TensorFlow in Google Colab, and evaluation using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R² Score. Results show the model achieved an MAE of 5.31 and an RMSE of 6.94, indicating moderate prediction error. However, the R² value of 0.0711 suggests very low explanatory power, potentially due to underfitting or data limitations. Notably, multi-step forecasting was applied to generate projections for 2026–2027 despite having historical data only through 2024, with these extended forecasts intended as experimental. The model successfully learned seasonal patterns but requires further optimization to improve predictive accuracy. This study advances AI-based inventory management for SMEs, supporting operational efficiency, waste reduction, and risk mitigation in raw material supply chains.