Dwi Marisa Midyanti, Syamsul Bahri, Ilhamsyah Ilhamsyah, Zalikhah Khairunnisa, Hafizhah Insani Midyanti
Indoor air quality (IAQ) has become a problem because it affects human health, comfort, and productivity. Predicting air quality is a complex task due to the dynamic nature of IAQ variable values simultaneously. In this study, the single hidden layer feedforward neural networks model is used, namely radial basis function (RBF), self-organizing maps (SOM)-RBF, and extreme learning machine (ELM) to classify IAQ. This study also observed the effect of the number of neurons in the hidden layer on the model accuracy and overfitting of each network. The experimental results show that the number of neurons in the hidden layer can affect the accuracy of the RBF and SOM-RBF models. Among the three models used, RBF produces very good training data accuracy but also the most significant overfitting value. The largest overall accuracy was obtained using SOM-RBF, with a value of 86.377%. © This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/
Department of Computer Engineering, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Pontianak, Indonesia; Department of Information Systems, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Pontianak, Indonesia; Department of Informatics Engineering, Faculty of Engineering, Universitas Tanjungpura, Pontianak, Indonesia; Faculty of Art and Design Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
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