relation: https://digilib.uin-suka.ac.id/id/eprint/57596/
title: Housing Price Prediction Using a Hybrid Genetic Algorithm with  Extreme Gradient Boosting
creator: Siregar, Maria Ulfah
creator: Hardjita, Pahlevi Wahyu
creator: Asdin, Farhan Armawan
creator: Wardani, Dewi
creator: Wijayanto, Ardhi
creator: Yunitasari, Yessi
creator: Anshari, Muhammad
subject: Tehnik Informatika
description: Predicting property prices provides a better service for customers to  evaluate and estimate price movement before their purchases. Some  features including OverallQual and GrLivArea, which were selected  when applying GA, become important features that can influence  property prices. This research proposes a hybrid Genetic algorithm  combined with the Extreme Gradient Boosting algorithm to predict  real estate housing prices. The proposed scheme is evaluated by  Root Mean Square Error, processing time, and the number of deleted  features. The proposed scheme has been compared with the sole  Extreme Gradient Boosting. The experimental results show that  the proposed scheme produces the smallest root mean square error  value of 0.129 compared to 0.133 of the sole Extreme Gradient  Boosting. Furthermore, the predicted time of the proposed scheme  is much better than the sole method.
date: 2023-02-27
type: Conference or Workshop Item
type: PeerReviewed
format: text
language: en
identifier: https://digilib.uin-suka.ac.id/id/eprint/57596/1/house%20prediction.pdf
identifier:   Siregar, Maria Ulfah and Hardjita, Pahlevi Wahyu and Asdin, Farhan Armawan and Wardani, Dewi and Wijayanto, Ardhi and Yunitasari, Yessi and Anshari, Muhammad  (2023) Housing Price Prediction Using a Hybrid Genetic Algorithm with Extreme Gradient Boosting.  In: The 2022 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 22-23 November 2022, Online.     
relation: https://dl.acm.org/doi/abs/10.1145/3575882.3575939