Penerapan Support Vector Regression untuk Estimasi Harga Properti
DOI:
https://doi.org/10.63822/7vmc5k96Keywords:
Estimation; House Price; RapidMiner; Support Vector RegressionAbstract
The rapid development of the property sector has increased the need for accurate house price estimation to support decision-making for buyers, sellers, and property developers. House prices are influenced by various factors, such as building age, location, accessibility to public transportation, and surrounding facilities, which makes price estimation a complex problem. This study aims to apply the Support Vector Regression (SVR) algorithm to estimate property prices based on historical data. The dataset used in this research is the Real Estate Valuation Dataset, consisting of 414 records with several numerical attributes related to property characteristics. The modeling and evaluation process was conducted using RapidMiner Studio. The dataset was divided into training data and testing data to build and evaluate the regression model. Model performance was measured using the Root Mean Squared Error (RMSE) metric. The experimental results show that the SVR model achieved an RMSE value of 3.281, indicating a relatively low estimation error. These results demonstrate that Support Vector Regression is capable of modeling the relationship between property attributes and prices effectively and can be used as a reliable method for property price estimation.
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