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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Khairunnisa, Zalikhah
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Computer Science & IT Engineering

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Single hidden layer feedforward neural networks for indoor air quality prediction Midyanti, Dwi Marisa; Bahri, Syamsul; Ilhamsyah, Ilhamsyah; Khairunnisa, Zalikhah; Midyanti, Hafizhah Insani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp322-328

Abstract

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 86.37%.
Co-Authors Dwi Marisa Midyanti Ilhamsyah Midyanti, Hafizhah Insani Syamsul Bahri