Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling - Purnawan, Hendra; Putra, Ryan; Fauzi, Rifqi; Setiawan, Antonius; Jaenul, Ariep; Al-Hakim, Rosyid; Nugroho, Habibie; Kuntjoro, Yanif (Jurnal Ilmiah Informatika dan Komputer, 2024)
Sales predictions on building material products today have applied an artificial neural network approach. One of the products of building material that need to be predicted for sales is polyvinylchloride (PVC) ceilings. Most companies haven’t implementing prediction technique for the sale of PVC ceilings, so this study aims to predict PVC ceiling sales with the backpropagation neural network (BPNN) method using the R algorithm. Unit gradients are calculated using the average absolute per cent error value (MAPE) to minimize the total square errors of network output. The results showed that the network architecture used was 4 to 6-1 and obtained an accuracy of 88% based on the lowest MAPE value.