Prediksi Jumlah Kedatangan Pasien Puskesmas Menggunakan Metode Backpropagation Artificial Neural Network - Sandi Satria Alamsyah; Maimunah, Maimunah; Pristi Sukmasetya (STMIK Budi Darma, 2024)
Patient visit prediction is a crucial aspect of community health center management to optimize the allocation of available resources. However, the erratic pattern of patient visits often complicates the planning and decision-making processes. This research aims to develop a patient visit prediction model for Grabag 1 Community Health Center using the backpropagation artificial neural network method. Backpropagation is a learning algorithm technique used in artificial neural network models with multiple hidden layers. In this research, there are several stages of data processing, including selecting the data attributes used, handling missing values, data normalization, sliding window, and dividing the data into training and testing sets. This prediction can be utilized by the health center to efficiently plan resource requirements, such as scheduling medical staff, managing medication supplies, and maintaining supporting facilities. The data used in this research is a time series spanning from 2019 to 2023. After conducting various experiments, the best results were obtained using a combination of 500 epochs, 30 input neurons, 1 hidden layer, 7 hidden neurons, and 1 output neuron. This artificial neural network architecture configuration achieved a Mean Squared Error (MSE) of 0.00229501 for the training data and 0.00782101 for the testing data.