Artificial Neural Networks Android-Based Interface Facial Recognition Systems

Yuwono, Kevin Alamsyah; Safitri, Irma; Tritoasmoro, Iwan Iwut

doi:10.1109/isriti48646.2019.9034569

Cited by 1 publication

(1 citation statement)

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“…The results demonstrated that trainrp provided optimal results and was more accurate than trainbr and trainlm. Finally, Yuwono et al [31] reported that the accuracy rate of the traincgp function was 18% higher than that of the trainrp function in the face recognition system of computing systems. The difference in accuracy between training functions is due to differences in the number of neurons, learning rate, and momentum in each simulation.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Training Parameters Combination of ANN Backpropagation for Climate Classification

Syaharuddin¹,

Fatmawati²,

Suprajitno³

2022

MMEP

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Artificial Neural Networks are widely used in prediction activities and classification processes. However, the implementation on average only uses a network architecture with one hidden layer, while the development of architectures with two or three hidden layers has not been done much. This article discusses the process of developing ANN Backpropagation using a Matlab-based graphical user interface with three hidden layers combined with non-linear activation functions (logsig, tansig, tanh) and training functions (trainrp and trainlm) based on learning rate and momentum. Architecture was created to study climate change in the area around Lombok International Airport station by training on hydrological data (rainfall) from January 2012 to December 2021 with a data type of 10-day interval (36 data every year). The number of neurons in the first hidden layer was determined using the Hecht-Nielsen model, while the second and third hidden layers used the Lawrence-Fredrickson model. Simulation results with architecture 36-73-37-19-1, a learning rate of 0.1, and momentum of 0.9 showed that variations in the activation function logsig-logsig-logsig-purelin and trainlm function demonstrated the best result with epoch of 7, MSE of 0.00090, and RMSE of 0.03011 in the training process and epoch of 5, MSE of 0.003758, and RMSE of 0.0613 in the data testing process. Furthermore, the prediction results demonstrated that a Q-value of 0.222 based on the Schmidt-Ferguson criteria obtained higher rainfall intensity information than previous years with climate category B (wet). Therefore, the government must be careful in determining policies related to field activities especially in agriculture because of climatic conditions with high rainfall.

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Section: Introductionmentioning

confidence: 99%