Implementation of Neural Network Algorithms in Predicting Student Graduation Rates - Iin Fiqha; Gomal Juni Yandris; Fitri Aini Nasution (Politeknik Ganesha Medan, 2021)
Higher education institutions are required to be providers of quality education. One of the instruments used by the government to measure the quality of education providers is the number of graduates. The higher the graduation rate, the better the quality of education and this good quality will positively affect the accreditation value given by BAN-PT. Therefore, in this study, researchers provide input for research conducted at Bhayangkara University, Greater Jakarta to predict student graduation rates using the Neural Network algorithm. Neural Network is a method in machine learning developed from Multi Layer Perceptron (MLP) which is designed to process two-dimensional data. Neural Network is included in the type of Deep Neural Network because of the depth of the network level and is widely implemented in image data. Neural Network has two methods; namely classification using feedforward and learning stages using backpropagation. The way Neural Network works is similar to MLP but in Neural Network each neuron is represented in two dimensions, unlike MLP where each neuron is only one dimension. The prediction accuracy obtained is 98.27%. unlike MLP where each neuron is only one-dimensional. The prediction accuracy obtained is 98.27%. unlike MLP where each neuron is only one-dimensional. The prediction accuracy obtained is 98.27%.