Mapping monthly consumer purchasing patterns at the UNHAN RI Cooperative using time series analysis and LSTM
DOI:
https://doi.org/10.35335/mandiri.v14i3.488Keywords:
ARIMA, Consumer Purchasing Pattern, Demand Prediction, Inventory Planning, LSTMAbstract
This study investigated the monthly purchasing patterns of consumers at Koperasi Unhan RI and developed forecasting models to support data-driven inventory and procurement planning. Historical cooperative sales data from 2020–2024 were analyzed using time series decomposition, autocorrelation analysis, ARIMA modeling, and a Long Short-Term Memory (LSTM) neural network. The analysis revealed a clear upward trend and strong annual seasonality, with consistent demand peaks occurring in December. The ARIMA model achieved significantly lower prediction errors than the LSTM model and successfully captured both trend and seasonal components. A 12-month forecast for 2025 was then generated to support operational decision-making. The forecasting results provide practical managerial insights for cooperative management, particularly in optimizing inventory levels, scheduling procurement, and anticipating seasonal demand fluctuations. The novelty of this study lies in the comparative application of classical time-series and deep learning approaches within a cooperative context using limited historical data, demonstrating that ARIMA remains a robust and interpretable solution for small to medium-sized cooperative environments. This research concludes that time series analysis combined with ARIMA forecasting effectively mapped consumer purchasing patterns and produced actionable demand predictions for the subsequent year.
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Copyright (c) 2025 Miranda Bintang Maharani Sigalingging, M. Azhar Prabukusumo Prabukusumo, Jonson Manurung
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