<![CDATA[The recovering tourism industry in Bali has triggered a surge in accommodation demand, but this also creates the problem of daily rental price inconsistency. Subjective pricing by property managers often leads to price disparities in the market. Previous studies generally used linear regression or Random Forest for property price prediction, but not many have integrated hyperparameter optimization using Optuna and mobile implementation for accommodation price estimation in Bali. Therefore, the novelty and main contribution of this study is the integration of the XGBoost model optimized using Optuna into a mobile application to generate real-time accommodation price estimations. This study aims to implement the XGBoost algorithm to estimate the rental prices of accommodations (hotels, villas, and resorts) in Bali based on location and facility variables. The research data was obtained through web scraping on Online Travel Agent (OTA) platforms, resulting in 4,618 clean data rows. To optimize accuracy and prevent overfitting, a Hyperparameter Tuning technique using the Optuna framework was applied to find the best parameter combination. The model's performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) metrics. The results showed the optimized XGBoost model achieved high accuracy with an R² of 0.797, RMSE of 0.3752, and MAE of 0.3007. System testing through a User Acceptance Test (UAT) also recorded an acceptance rate of 89.2% (Very Good). In conclusion, the developed prediction model is highly accurate and applicable as an objective price reference instrument for both property managers and tourists.]]>
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