Penerapan Decision Tree Regression dalam Memprediksi Harga Rumah di Provinsi Jawa Barat - Almajid, Nafis; Ginting, Yanuar; Ramadhan, Alif Izzudin (Jejaring Penelitian dan Pengabdian Masyarakat (JPPM), 2024)
A house plays an important role as a basic necessity, not only as a shelter and place of rest. Additionally, the selling price of a house is greatly influenced by environmental factors such as proximity to shopping centers, office buildings, land size, and others. To gain a more accurate understanding and predict the selling price of houses, this research proposes the application of machine learning methods. This study involves the use of three machine learning algorithms: multiple linear regression, decision tree regression, and linear support vector regression to predict house prices in West Java Province. By utilizing historical data and various relevant features, such as the number of rooms, bathrooms, garages, land area, and width, the machine learning algorithms will conduct complex analyses and provide more accurate price estimations. The expected outcome of this research is to provide valuable insights for stakeholders in the property industry in West Java Province. The adoption of machine learning approaches is anticipated to enhance the ability to predict house prices and provide useful information for buyers, sellers, and other relevant parties in making better decisions in property transactions.