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dc.contributor.authorKokasih, Marco Febriadi
dc.contributor.authorParamita, Adi Suryaputra
dc.date.accessioned2023-01-24T02:30:49Z
dc.date.available2023-01-24T02:30:49Z
dc.date.issued2020
dc.identifier.issn2579-7069
dc.identifier.urihttp://dspace.uc.ac.id/handle/123456789/5740
dc.description.abstractOnline marketplace in the field of property renting like Airbnb is growing. Many property owners have begun rentingouttheir properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variablethatwill be used for this study is listing feature, neighbourhood, review, date and host information. Predictionmodel iscreated based on the dataset given by the user and processed with Extreme Gradient Boosting algorithmwhichthenwillbe stored in the system. The result of this study is expected to create prediction models for property rent priceforproperty owners and tourists consideration when considering to rent a property. In conclusion, Extreme GradientBoosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.en_US
dc.publisherBright Publisheren_US
dc.subjectRental Priceen_US
dc.subjectPrediction Modelen_US
dc.subjectExtreme Gradient Boostingen_US
dc.subjectXGBoosten_US
dc.titleProperty Rental Price Prediction Using the Extreme Gradient Boosting Algorithmen_US
dc.typeArticleen_US


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