Wima Puspita, Defrianto Defrianto, Yan Soerbakti
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引用次数: 2

摘要

本研究的目的是利用基于天气因素的反向传播人工神经网络(ANN)预测北干巴鲁的颗粒物(PM10)水平。数据采用2014 - 2017年的数据作为训练数据,2018年的数据作为测试数据。该结构由5 - 5 - 1个神经元组成,并使用log -log -purelin函数。训练过程产生一个MSE值较小的traincgb,在测试PM10预测过程中,与BMKG数据相比,平均误差为26.9062%。
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PREDIKSI KADAR PARTICULATE MATTER (PM10) MENGGUNAKAN JARINGAN SYARAF TIRUAN DI KOTA PEKANBARU
This aims of this study is to predict particulate matter (PM10) levels in Pekanbaru using back propagation artificial neural networks (ANN) based on weather factors. The data used in the form of data from 2014 – 2017 as training data and 2018 data as test data. The architecture proposed is composed of 5 – 5 – 1 neurons and uses the logig-logsig-purelin functions. The training process produces a traincgb with a small MSE value and in the process of testing the PM10 prediction compared to BMKG data has an average error of 26.9062%.
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