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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang
Adi Nugroho, Anjasmoro
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Decision Sciences, Operations Research & Management

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DECISION TREE-BASED GRADIENT BOOSTING: ALGORITHM TO APPROACH HOUSE PRICE PREDICTION IN JAKARTA, BOGOR, DEPOK, TANGERANG, BEKASI (JABODETABEK) Lisnawati, Intan; Adi Nugroho, Anjasmoro
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 2 (2024): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.2.2024.1-9

Abstract

The house sale prices are a particular concern for some people, whether sellers or buyers, for personal use or investment. Commonly, the buyer comes from newly-married couples, parents, or investors. Compared to years ago, the recent price is more expensive due to some conditions over the time. Forecasting is a method to see at which price the house may fit the market price with certain features. Through this study, we complement the previous research about house prices and analyze the results. Besides, here we also break down the algorithm and sketch the steps so that it eases the reader to understand the method. Exploratory data analysis is also done to see and analyze the characteristics of the dataset. Applying decision tree-based gradient boosting, we run the algorithm into datasets in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) consisting of house price and its features. We see that the RMSE value is Rp277.369.397 and the MAPE is 17,3%. With that value of accuracy we could mention that gradient boosting is quite competitive compared with other methods and able to give its best prediction over house prices.
Co-Authors Lisnawati, Intan