Backpropagation Neural Network Untuk Prediksi Kebutuhan Pemakaian Obat (Kasus Di RSUD dr. Adnaan WD) - Hazlita, H; Defit, Sarjon; Nurcahyo, Gunadi Widi (Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 2024)
Artificial Intelligence which is developing increasingly rapidly makes it possible to make predictions. Predictions are made using one of the Artificial Intelligence systems, namely Artificial Neural Networks. Predicting the need for drug use is a problem currently being faced by RSUD dr. Adnaan WD Payakumbuh so that the service is not optimal. This research aims to design an Artificial Neural Network architecture and determine the resulting level of accuracy in predicting the need for drug use. The method used in this research is the Backpropagation method. The stages in the Backpropagation algorithm include the initial weight initialization process, activation stage, weight change and iteration stage. The data processed in this research is drug use data obtained from the Pharmacy Installation at dr. Adnaan WD Payakumbuh Hospital. The results of this research show that the best network architecture is 12-12-1 with a relatively small Mean Squared Error (MSE) value of 0.00685, a Mean Absolute Percentage Error (MAPE) value of 0.1696% and a high level of accuracy reaching 99 .83% for the prediction of Paracetamol 150 mg. The results of this research can help health service centers optimize their services