Sales Forecasting of Oyster Mushrooms Using Simple LinearRegression: A Case Study of Tugumukti SMEs
DOI:
https://doi.org/10.30812/bite.v7i2.5572Keywords:
Oyster Mushroom, Sales Prediction, Simple Linear RegressionAbstract
Background: One of the primary challenges in oyster mushroom cultivation is the imbalance between production capacity
and market demand, as business owners struggle to forecast sales accurately.
Objective: This study aims to test the predictive power of simple linear regression for white oyster mushroom sales at the
SME level.
Methods: This study uses a simple linear regression model, using 20 months of historical sales data, split into training
and test sets at 80:20, with time as the predictor variable.
Result: The evaluation resulted in a Mean Absolute Error (MAE) value of 775,203, Root Mean Squared Error (RMSE)
of 813,411, and Mean Absolute Percentage Error (MAPE) of 13.05%, which is categorised as good.
Conclusion: This study contributes to the literature on agricultural commodity forecasting, particularly oyster mushrooms,
by demonstrating the relevance of simple linear regression. These findings have implications for accurate production
planning and reducing the risk of overproduction.
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