Sistem Prediksi Permintaan Barang Berat dengan Memanfaatkan Data Historis Penjualan Menggunakan Long Short-Term Memory (LSTM)TERM MEMORY (LSTM)

Authors

  • Ginda Maruli Andi Siregar Universitas Samudra
  • Khairul Anam Universitas Samudra
  • Saiyaratul Mawaddah Universitas Samudra

DOI:

https://doi.org/10.31599/w0zqt762

Keywords:

Building Material, Demand Forecasting, Inventory Management, Historical Sales Data ,Long Short-Term Memory

Abstract

Inventory management is an important aspect in maintaining the effectiveness of business 
operations, particularly in building material stores where demand fluctuations can affect stock 
availability. Inaccurate inventory planning may lead to overstock or stockout conditions, resulting 
in increased operational costs and reduced customer satisfaction. This study aims to develop a 
demand forecasting system for building materials using the Long Short-Term Memory (LSTM) 
method based on historical sales data at Toko Bangunan Beu Sukses. The dataset used 
consists of daily sales data from January 2024 to October 2025 covering six products, namely 
steel, cement, paint, pipes, zinc roofing, and plywood. Data preprocessing was performed 
through logarithmic transformation, differencing, normalization using MinMaxScaler, and 
sequence formation using the sliding window method. The LSTM model was trained and 
evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean 
Absolute Percentage Error (MAPE). The evaluation results indicate that the proposed model 
achieved a high level of forecasting accuracy, with all products obtaining MAPE values below 
2%. Furthermore, the developed model was successfully integrated into a web-based 
application to support inventory management and decision-making processes. The results 
demonstrate that the LSTM method can effectively predict building material demand and 
support more efficient inventory management. 

 

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Published

2026-07-31

Issue

Vol. 7 No. 1 (2026): Juni 2026

Section

Artikel

License

Copyright (c) 2026 Ginda Maruli Andi Siregar, Khairul Anam, Saiyaratul Mawaddah

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How to Cite

Sistem Prediksi Permintaan Barang Berat dengan Memanfaatkan Data Historis Penjualan Menggunakan Long Short-Term Memory (LSTM)TERM MEMORY (LSTM). (2026). Journal of Informatic and Information Security, 7(1), 23-36. https://doi.org/10.31599/w0zqt762