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All Journal Unisda Journal of Mathematics and Computer Science (UJMC)
Shofwan Ali Fauji
Universitas Islam Darul 'Ulum Lamongan
Computer Science & IT Education Mathematics

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ANALISIS FUNGSI AKTIVASI JARINGAN SYARAF TIRUAN UNTUK MENDETEKSI KARAKTERISTIK BENTUK GELOMBANG SPEKTRA BABI DAN SAPI Shofwan Ali Fauji; Ari Kusumastuti
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 1 No 01 (2015): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2057.225 KB) | DOI: 10.52166/ujmc.v1i01.437

Abstract

Artificial Neural Network (ANN) is beginning little by little to replace the task of an expert, even with the ANN can be a tool to replace a doctor. One of kind of ANN is backpropagation networks, this network can be used to training programs in order to be able to recognize whether it is pig or cow wave spectra. To determine the output in backpropagation training required suitable activation functions. Therefore, in this research will be compared to some of the activation function that can be used in training. Activation functions will be tested with the ratio test to determine the interval convergence. After tested with the ratio test it was found that the activation function tanh z was the best activation function to use thebackpropagation network training, because it has a weight range that can meet the methods used in the determination of weights. When tested with the data, the activation function tanh z is able to recognize correctly all trial datas. An expected in future research to examine the weight that makes the interval training to achieve fast convergence and the error bit.
Co-Authors Ari Kusumastuti