PENERAPAN METODE JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK MEMPREDIKSI NILAI UJIAN SEKOLAH

Publication Type:

Journal Article

Authors:

SANDY KOSASI

Source:

Jurnal Teknologi, Volume 7, Number 1 (2014)

Abstract:

Abolishment policy National Examination for the primary level/equivalent during the period of 2013-
2014 have consequences on the growing position of the Examination Schools, particularly in the
determination and measurement of competency graduation students. Therefore, as a preparation
for the war against it, the author makes an application of artificial neural networks using back
propagation method for predicting the value of the Examination Schools elementary school
students. This research using case study design, located at SDN 1 Central Singkawang and using
experimental methods. A research variable grades of Mathematics and Science subjects as well as
the value of the Examination Schools on both these subjects. Methods of design and development
using prototyping model. The results showed that the value of the Mean Square Error (MSE), the
smallest in Mathematics obtained at 0.5100175 with a combination of parameters in the form of
26,000 training epochs and learning rate of 0.5. In science subjects, the smallest MSE value
obtained through a combination of 0.1405143 1,000 epochs of training parameters and the value of
learning rate 0.9. The average accuracy rate of the network output obtained for 80.15 %. It can be
concluded that backpropagation neural network produced reliable enough to predict school test
scores of elementary school students.

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