Arabica Coffee Sales Forecasting Using ARIMA Neural Network (Case Study: KOTEM Bondowoso)
Author's Country: Indonesia
DOI:
https://doi.org/10.36805/5a61hr55Keywords:
Arima, Coffee sales, Forecasting, Neural Network, MAPEAbstract
Kopi Tembakau (KOTEM) Bondowoso is a local coffee producer specializing in Arabica ground coffee, sourced directly from nearby farmer cooperatives. A major hurdle they face is accurately forecasting sales, a critical factor for optimizing production and inventory. To tackle this, a hybrid forecasting model blending ARIMA (for linear/seasonal trends) and Neural Networks (for non-linear patterns) was developed. The study analyzed KOTEM’s sales data from September 2019 to August 2022, preprocessed to address non-stationarity via differencing and normalization. Results revealed the hybrid model outperformed standalone ARIMA, achieving a 1.0% MAPE (vs. ARIMA’s 1.3%). It also better captured sales volatility and seasonal shifts, offering more dependable forecasts. ARIMA-NN could significantly enhance KOTEM production scheduling and stock management.
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