Analysis of Satisfaction Level with Servqual method using Artificial Neural Networks

Abstract

This research aims to analyze the level of student satisfaction at a college in pattern recognition using an artificial neural network with feedforward backpropagation algorithms. Input on the network consists of 5 variables, namely Tangibles (X1), Reliability (X2), Responsiveness (X3), Assurance (X4) and Empathy (X5) which are the dimensions of the Servqual method. Data processing in the Servqual method will become target data on the network. Data is obtained through multi-item questions given to students. After some training trials, the best configuration architecture consists of 5 neurons in the input layer, one hidden layer with seven neurons and one neuron in the output layer. The activation function used by a Sigmoid Binary that has a range of 0 to 1. This architecture can recognize the pattern well in the 14th iteration with MSE of 0.000192. Data testing results obtained an accuracy rate of 91.07% with an error rate of 8.93%. So that it can be concluded that the Artificial Neural Network with feedforward backpropagation algorithm in the case of analyzing the level of student satisfaction on college services can recognize the pattern well.