Prediksi Angka Partisipasi Sekolah dengan Fungsi Pelatihan Gradient Descent With Momentum & Adaptive LR

Abstract

<p class="Abstract">School Participation Rate (APS) is known as one of the indicators of the success of the development of educational services in regions both Province, Regency or City in Indonesia. The higher the value of the School Participation Rate, then the area is considered successful in providing access to education services. The purpose of this study is to predict School Participation Rates based on Provinces in Indonesia from Aceh to Papua. The prediction algorithm used is the backpropagation algorithm using the gradient descent with momentum & adaptive LR (traingdx) training function. Traingdx is a network training function that updates weight values and biases based on gradient descent momentum and adaptive learning levels. Usually, the backpropagation algorithm uses the gradient descent backpropagation (traingd) function, but in this study, the training function used is using gradient descent with momentum & adaptive LR (traingdx). The data used in this study data on School Participation Figures for each province in Indonesia in 2011-2017 aged 19-24 years were taken from the Indonesian Central Bureau of Statistics (BPS). The reason for choosing this age range is because at this age is one of the factors that determine the success of education in a country, especially Indonesia. This study uses 3 network architecture models, namely: 5-5-1, 5-15-1 and 5-25-1. Of the 3 models, the best model is 5-5-1 with an iteration of 130, the accuracy of 94% and MSE 0,0008708473. This model is then used to predict School Participation Rates in each province in Indonesia over the next 3 years (2018-2020). These results are expected to help the Indonesian government to further increase scholarships and improve the quality of education in the future..</p><p> </p><p class="IndexTerms"><strong>Keywords</strong>: Prediction, APS, Backpropagation, Traingdx.</p>

Implementation of Neural Network in Early Detection of Financial Crisis in Singapore

“…SGD optimization is used to find local DOI 10.18502/kls.v8i1.15587 ICMScE minimum values generated from parametric functions so as to avoid local minimums and find global minimums. However, for large data, SGD optimization tends to take a long time to run the algorithm [12]. Timothy Dozat [13] introduced the Nesterovaccelerated Adaptive Moment Estimation (Nadam) optimization, which is an adaptive learning rate method that uses a different learning rate.…”