Implementation and Evaluation of Artificial Neural Networks for Product Sales Prediction at Basmalah Stores
DOI:
10.33395/sinkron.v9i4.15341Keywords:
Sales prediction, artificial neural network, MSE, time series, toko basmalahAbstract
This study aims to develop a product sales prediction system for Toko Basmalah located in the Malang Regency area by utilizing the Artificial Neural Network (ANN) algorithm. A quantitative approach was employed, using time series sales data obtained from the Marketing Division of PT. Sidogiri Pandu Utama for the period of January 1, 2023, to December 31, 2024. The research stages included data collection and preprocessing, normalization using the min-max scaling technique, data splitting into training and testing sets, ANN model experimentation with various data compositions, and performance evaluation based on the Mean Squared Error (MSE) metric. The experiments were conducted five times using the Kaggle Editor platform. The results showed that the ANN-E model with a specific architecture achieved the lowest MSE value of 34.38%, making it the most optimal model for sales prediction. These findings are expected to assist in making better decisions regarding stock management, sales planning, and business strategies in the retail environment.
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Copyright (c) 2025 Muhammad Iqbal Akkad, Mokhamad Amin Hariyadi, Agung Teguh Wibowo Almais
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