MODEL PREDIKSI KEPAILITAN BANK UMUM DI INDONESIA MENGGUNAKAN ALGORITMA BACKPROPAGATION
Abstract: The
early warning model
was built using
13 CAMEL ratios. The method used is the neural network
with backpropagation. Networks are built
using a hidden layer and bipolar sigmoid activation function. In this study,
the training phase trials performed 12 times by combining the rate of
learning rate and
iteration to find
the best network
model. The value of learning rate
is 0.1, 0.3, 0.5 and 0.7 combined with 1000, 2000 and 5000
iterations. The result
found that the
combination of learning rate 0.7 and iteration 2000 as the
best model with 100% accuracy and computational time 21 seconds. The resulting
output value compared to the actual status of the bank. As a result, the
network model is able to produce an accuracy of 86.11%.
Penulis: ADITYA SETIAWAN
MALAKA, HARTOJO
Kode Jurnal: jpmanajemendd141015
