Metode Peramalan Jaringan Saraf Tiruan Menggunakan Algoritma Backpropagatin (Studi Kasus Peramalan Curah Hujan Kota Palembang)

I M Sofian, Y Apriaini

Abstract


Penelitian bertujuan untuk memprediksi curah hujan bulanan menggunakan jaringan syaraf tiruan (JST) dengan suatu fungsi pelatihan backpropagation. Penelitian ini menggunakan data curah hujan di Stasiun Klimatologi Kelas I Palembang dari tahun 2014 sampai tahun 2016. Analisis dilakukan terhadap tingkat korelasi antara output jaringan dengan data observasi dan dari nilai MSE yang dihasilkan jaringan. Hasil penelitian menunjukkan jaringan terbaik dengan jumlah neuron 12 pada lapisan input, pada lapisan tersembunyi terdapat 3 lapis terdiri dari 50-20-20 neuron, 1 neuron pada lapisan output, data latih tahun 2014 dengan target tahun 2015, data uji tahun 2015 dengan target tahun 2016. Adapun parameter JST lr=0,1, mc=0,9, epochs=1000, te=20, e=0,001 yang mempunyai korelasi terhadap data observasi sebesar 0,99276 dengan nilai MSE 0,00086145 (proses pelatihan). Sementara pada proses pengujian, korelasi terhadap data observasi sebesar 0,79544 dengan nilai MSE 0,25528. Jaringan ini kemudian digunakan untuk proses prediksi curah hujan tahun 2017.

The study aims to predict monthly rainfall using artificial neural network (ANN) with a backpropagation training function. This research uses rainfall data at Climatology Station Class I Palembang from 2014 until 2016. The analysis is done to determine correlation level between network output with observation data and from value of MSE produced by network. The results show the best network with the number of 12 neurons in the input layer, in the hidden layer there are 3 layers consisting of 50-20-20 neurons, 1 neuron in the output layer, training data of 2014 with a target of 2015, test data of 2015 with the target year 2016. The JST parameter lr = 0,1, mc = 0,9, epochs = 1000, te = 20, e = 0,001 have correlation to observation data equal to 0,99276 with value of MSE 0.00086145 (training process). While in the testing process, the correlation to observation data of 0.79544 with the value of MSE 0.25528. This network is then used for the rainfall prediction process in 2017.


Keywords


Artificial neural network, backpropagation, rainfall

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References


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