An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction

Priambodo, Fikri Nurqahhari and Sihabuddin, Agus (2020) An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 11 (11). pp. 179-185. ISSN 2158-107X

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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.

Item Type: Article
Uncontrolled Keywords: Airbnb; base price prediction; extreme learning machine; fast learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department
Depositing User: Sri JUNANDI
Date Deposited: 02 Jun 2025 07:50
Last Modified: 02 Jun 2025 07:50
URI: https://ir.lib.ugm.ac.id/id/eprint/17270

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