<![CDATA[Jimbaran District is one of the strategic areas in Badung Regency, Bali, experiencing rapid growth in the education, tourism, and economic sectors. This condition has increased the demand for temporary housing, especially boarding houses for students and migrant workers. The large number of boarding house options with various facilities and rental prices often makes it difficult for prospective tenants to choose accommodations that match their needs and financial capabilities. This study aims to develop a web-based boarding house price prediction application using the Decision Tree Regression algorithm to assist users in estimating boarding house prices based on the desired facilities. The research applies a quantitative approach using machine learning and data mining methods. The dataset consists of boarding house data in the Jimbaran area, including room size, bathroom type, room facilities, public facilities, distance to campus, and monthly rental price. The research stages include data preprocessing, feature transformation, dataset splitting using the Train-Test Split method, model training, and model evaluation using MAE, RMSE, and R² metrics. The results show that the Decision Tree Regression algorithm achieved good performance with an R² value of 0.8332, an MAE value of IDR 225,462, and an RMSE value of IDR 302,509.]]>
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