Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction

Abstract

Gradient Descent (GD) is used to find the local minimum value, its purpose is to find variables on the error function so that a function can model the data with minimum error. Therefore, the purpose of this research is to see how much iteration is needed and how big is the accuracy level in predicting the data when using Gradient Descent (GD) Standard and GD With Momentum and Adaptive Learning Rate (GDMALR) functions. In this study, the data to be processed using the gradient descent function is the data of School Participation Rate (SPR) in Indonesia aged 19-24 years, which began in 2011 to 2017. The reason for selection This age range is one of the factors that determine success education in a country, especially Indonesia. SPR is known as one of the indicators of successful development of education services in an area of either Province, Regency or City in Indonesia. The higher the value of SPR, then the area is considered successful in providing access to education services. SPR data are taken from Indonesian Central Bureau of Statistics. This study uses 3 models of network architecture, namely: 5-5-1, 5-15-1 and 5-25-1. From 3 models, the best model is 5-5-1 with epoch 6202 iteration, 94% accuracy and MSE 0.0008658637. This model is then used to predict SPR in Indonesia for the next 3 years (2018-2020). These results will be expected to help the Indonesian government to further improve the scholarship and improve the quality of education in the futur