Application of General Regression Neural Network Algorithm in Data Mining for Predicting Glass Sales and Inventory Quantity


Suryani Suryani(1*); Indo Intan(2); Farhan Mochtar Yunus(3); Adammas Haris(4); Faizal Faizal(5); Nurdiansah Nurdiansah(6);

(1) Univeristas Dipa Makassar
(2) Univeristas Dipa Makassar
(3) Univeristas Dipa Makassar
(4) Univeristas Dipa Makassar
(5) Univeristas Dipa Makassar
(6) Univeristas Dipa Makassar
(*) Corresponding Author

  

Abstract


FF Jaya Glass is a shop that supplies and installs 3 mm to 12 mm glass. The store obtained glass from suppliers to be processed in shape and size according to customers’ order. After completing the customer's order, the shop worker will install the glass at the requested location. Unfortunately, currently stores do not utilize sales data to predict sales either manually or by utilizing technology. As a result, the store cannot predict when the number of glass orders will increase or decrease. In addition, errors often occur when ordering glass for the next period. As a result, stores often run out of glass supplies due to the large number of glass orders so that the achievement of profits is not optimal. This study aims to identify sales variables in glass sales data and build a general regression neural network model as a data mining method. In addition, this study aims to iterate to find the best value in the sales data training process, design and create applications according to user needs, and conduct system validation tests. The general regression neural network method is used to predict sales. The results of this study indicate that the application of general regression neural networks can be used to predict sales. This will make it easier for the store to provide glass supplies in the coming months with an accuracy of 98.1%.


Keywords


Data Mining; General Regression Neural Network; Predicting; Sales; Supply

  
  

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doi  https://doi.org/10.33096/ilkom.v15i2.1562.229-239 		  

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