Resilient method in determining the best architectural model for predicting open unemployment in Indonesia

Abstract

Abstract The resilient method is a method of Artificial Neural Networks which is often used to make predictions, especially in times series data (continuous). This method is able to make predictions by learning from data that has been done before by first forming the right network architecture model. Therefore, this study will discuss the best network architectural models to make predictions using the resilient method. The data used in this study is open unemployment data according to the highest education completed in Indonesia in 2005 to 2018 based on the semester, which was sourced from the National Labor Force Survey (Sakernas) obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data, a network architecture model will be formed that is used with the Bayesian Regulation method, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18-2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0,0022135087 and 0,0044974696.