PREDIKSI NILAI TEMPERATUR UDARA MENGGUNAKAN METODE AUTOREGRESSIVE ARTIFICIAL NEURAL NETWORK DENGAN VARIASI JUMLAH HIDDEN LAYER

DEBATARAJA, CHRISTINE LUSIANA BORU (2022) PREDIKSI NILAI TEMPERATUR UDARA MENGGUNAKAN METODE AUTOREGRESSIVE ARTIFICIAL NEURAL NETWORK DENGAN VARIASI JUMLAH HIDDEN LAYER. S2 thesis, Universitas Mercu Buana - Menteng.

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Abstract

In this paper, we aim to predict the value of air temperature using the Autoregressive Artificial Neural Network (NARNN) method with variations in the number of hidden layers. The number of hidden layers used ranges from 1 to 20 hidden layers. As for the criteria for the results of the prediction data using MAPE. All MAPE values for the specified number of hidden layers are below 10%. From the experimental results obtained the smallest MAPE value in 12 hidden layers with a MAPE value of 3.06%. The largest MAPE value with the number of hidden layers is 4 hidden layers, which is 8.79%. The number of hidden layers affects the prediction value but no pattern has been found between the number of hidden layers and the accuracy of the prediction data. Keywords: Autoregressive Artificial Neural Network (NAR), hidden layer, air temperature, data prediction. Tulisan ini bertujuan untuk memprediksi nilai temperatur udara menggunakan metode Autoregressive Artificial Neural Network (NARNN) dengan variasi jumlah hidden layer. Jumlah hidden layer yang digunakan adalah 1 sampai 20 hidden layer. Adapun kriteria evaluasi hasil penelitian menggunakan MAPE. Semua nilai MAPE untuk jumlah hidden layer yang ditentukan di bawah 10%. Dari hasil percobaan didapatkan nilai MAPE terkecil pada 12 hidden layer dengan nilai MAPE sebesar 3,06%. Nilai MAPE terbesar dengan jumlah hidden layer adalah 4 hidden layer, yaitu 8,79%. Jumlah hidden layer mempengaruhi nilai prediksi tetapi tidak ada pola yang ditemukan antara jumlah hidden layer dan keakuratan data prediksi. Kata Kunci: Autoregressive Artificial Neural Network (NARNN), hidden layer, temperatur udara, data prediksi.

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