PERBANDINGAN MODEL JARINGAN SYARAF TIRUAN DENGAN ALGORITMA LEVENBERG-MARQUADT DAN POWELL-BEALE CONJUGATE GRADIENTPADA KECEPATAN ANGIN RATA-RATA DI KOTA SEMARANG

Dwi Ispriyanti(1), Alan Prahutama(2*), Tarno Tarno(3), Budi Warsito(4), Hasbi Yasin(5), Pandu Anggara(6)


(1) Departemen Statistika FSM Universitas Diponegoro
(2) Departemen Statistika FSM Universitas Diponegoro
(3) Departemen Statistika FSM Universitas Diponegoro
(4) Departemen Statistika FSM Universitas Diponegoro
(5) Departemen Statistika FSM Universitas Diponegoro
(6) Departemen Statistika FSM Universitas Diponegoro
(*) Corresponding Author

Abstract


Wind is one of the most important weather components. Wind is defined as the dynamics of horizontal air mass displacement measured in two parameters, namely speed and direction. Wind speed and direction depend on the air pressure conditions around the place. High wind speed intensity can cause high sea water waves. To estimate wind speed intensity required a study of wind speed prediction. One of method that can be used is Artificial Neural Network (ANN). In ANN there are several models, one of which is backpropagation. Thepurpose of this researchis to compare between backpropagation model with Levenberg-Marquadt and Powell-Beale Conjugate Gradient algorithms. The results of this researchshowing that Powell-Beale Conjugate Gradient better than Levenberg-Marquadtalgorithms. The best model architecture obtained is a network with two input layer neurons, six hidden layer neurons, and one output layer neuron. The activation function used are the logistic sigmoid in the hidden layer and linear in the output layer. MAPE value based on the chosen model is 0,0136% in training process and 0,0088% in testing process.


Keywords


Wind;Artificial Neural Network (ANN);Backpropagation;Levenberg-Marquadt;Powell-Beale Conjugate Gradient;Neuron;MAPE

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References


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DOI: https://doi.org/10.26714/jsunimus.8.2.2020.127-133

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