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All Journal International Journal of Artificial Intelligence for Digital Marketing
Azsyams, Luke Farrer
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Economics, Econometrics & Finance Environmental Science

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ANALYSIS OF THE LSTM MODEL ON THE DEMAND PATTERNS OF INDONESIAN TRADITIONAL COOKIES IN ONLINE MARKETPLACES Azsyams, Luke Farrer; Pebrianggara, Alshaf; Almanfaluti, Istian Kriya
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.419

Abstract

Objective:  This study aims to analyze the application of the Long Short-Term Memory (LSTM) model in predicting demand patterns for Indonesian culinary products in online marketplaces. Method: Using monthly sales data from January 2022 to May 2024, the model was trained and evaluated with the metrics Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². Results: The results showed an MSE of 899.70, an RMSE of 30.00, and an R² value of 0.09, indicating that the model has limitations in capturing variations in historical data. Nevertheless, LSTM still has potential as a forecasting tool for MSME entrepreneurs in decision-making related to inventory management, production planning, and marketing strategies. Novelty: Future research is recommended to expand the dataset, incorporate external factors such as seasonal trends and promotions, and explore hybrid approaches to improve prediction accuracy.
Co-Authors Almanfaluti, Istian Kriya Alshaf Pebrianggara