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DOI: 10.14569/IJACSA.2020.0111123
PDF

An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction

Author 1: Fikri Nurqahhari Priambodo
Author 2: Agus Sihabuddin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The base price of Airbnb properties prediction is still a new area of prediction research, especially with the Extreme Learning Machine (ELM). The previous studies had several suggestions for the advantages of ELM, such as good generalization performance, fast learning speed, and high prediction accuracy. This paper proposes how the ELM approach is used as a prediction model for Air BnB base price. Generally, the steps are setting hidden neuron numbers, randomly assigning input weight and hidden layer biases, calculating the output layer; and the entire learning measure finished through one numerical change without iteration. The performance of the model is estimated utilizing mean squared error, mean absolute percentage error, and root mean squared error. Experiment with Airbnb dataset in London with twenty-one features as input generates a faster learning speed and better accuracy than the existing model.

Keywords: Airbnb; base price prediction; extreme learning machine; fast learning

Fikri Nurqahhari Priambodo and Agus Sihabuddin, “An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111123

@article{Priambodo2020,
title = {An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111123},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111123},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Fikri Nurqahhari Priambodo and Agus Sihabuddin}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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