TY  - CONF
ID  - digilib57596
UR  - https://dl.acm.org/doi/abs/10.1145/3575882.3575939
A1  - Siregar, Maria Ulfah
A1  - Hardjita, Pahlevi Wahyu
A1  - Asdin, Farhan Armawan
A1  - Wardani, Dewi
A1  - Wijayanto, Ardhi
A1  - Yunitasari, Yessi
A1  - Anshari, Muhammad
Y1  - 2023/02/27/
N2  - 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.
KW  - featureselection
KW  -  RMSE
KW  -  crossover
KW  -  mutation
TI  - Housing Price Prediction Using a Hybrid Genetic Algorithm with
Extreme Gradient Boosting
SP  - 296
M2  - Online
AV  - public
EP  - 300
T2  - The 2022 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)
ER  -