Prediksi Harga Sewa Apartemen Di Kota Surabaya Menggunakan Metode Ensemble

Komaruljannah, Erlina (2023) Prediksi Harga Sewa Apartemen Di Kota Surabaya Menggunakan Metode Ensemble. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Peningkatan kebutuhan ruang permukiman mengharuskan sebuah kota untuk meningkatkan infrastruktur pembangunan supaya memenuhi kebutuhan tempat tinggal penghuninya. Dengan adanya apartemen akan membantu mengoptimalkan daya tampung lahan hunian dalam lahan yang terbatas terutama bagi daerah dengan kepadatan penduduk tinggi dan luas daerah yang kecil seperti Kota Surabaya. Hingga tahun 2020, tingkat permintaan hunian atau sewa di apartemen cenderung menurun dan setelah adanya pandemic COVID-19 membuat dampak yang sangat besar pada pasar apartemen dari sisi penjualan dan harganya yang menurun drastis. Perusahaan konsultan properti, Coldwell Banker Commercial menyatakan bahwa di antara kota besar lainnya hanya Surabaya yang memiliki kinerja lebih baik dalam pemasaran apartemen, karena tingkat penjualan dan harga rata-rata meningkat sebesar 0,4%. Melihat urgensi dan potensi melemahnya masyarakat modern dalam memilih hunian apartemen di Kota Surabaya, penulis membuat suatu penelitian yang dapat membantu dalam memprediksi harga sewa apartemen sesuai dengan kriteria apartemen di Surabaya seperti lokasi kecamatan, jenis unit, luas, nomor lantai, dan isi fasilitas unit. Data yang digunakan yaitu data dari travelio.com yang merupakan salah satu website terpercaya di Indonesia untuk penyewaan apartemen dengan metode scraping. Model prediksi harga sewa apartemen menggunakan metode ensemble didapatkan bahwa stacking model dengan kombinasi antara gradient boosting regression, random forest, decision tree regression, linear regression meta linear regression memiliki performansi yang lebih tinggi, dengan nilai MAE sebesar Rp 243.401, RMSE sebesar Rp 179.432 dan nilai R2 sebesar 63,28%, yang artinya mempengaruhi harga sewa apartemen sebesar 63,28%, sedangkan 36,72% dipengaruhi oleh faktor lain yang tidak dimasukan ke dalam model.
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Increasing the need for residential space requires in city to improve development
infrastructure in order to meet the needs of its inhabitants. Having an apartment will help optimize the capacity of residential land in limited areas, especially for areas with high population density and small areas such as the city of Surabaya. Until 2020, the level of demand for housing or rental in apartments tends to decline and after the COVID-19 pandemic made a huge impact on the apartment market from a sales perspective and prices dropped dramatically. A property consulting firm, Coldwell Banker Commercial, stated that among other big cities, only Surabaya had a better performance in apartment marketing, because the sales rate and average price increased by 0.4%. Seeing the urgency and potential for the weakening of modern society in choosing apartment housing in the city of Surabaya, the authors conducted a study that could assist in predicting apartment rental prices according to apartment criteria in Surabaya such as sub-district location, unit type, area, floor number, and unit facility contents. The data used is data from travelio.com which is one of the most trusted websites in Indonesia for apartment rentals using the scraping method. The apartment rental price prediction model using the ensemble method found that the stacking model with a combination of gradient boosting regression, random forest, decision tree regression, linear regression meta linear regression has higher performance, with an MAE value of IDR 243,401, RMSE of IDR 179,432 and a value R2 is 63.28%, which means that it affects the price of apartment rentals by 63.28%, while 36.72% is influenced by other factors that are not included in the model.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Harga Sewa, Apartemen, Bagging, Boosting, Stacking, Ensemble Learning; Rent Prices, Apartments, Bagging, Boosting, Stacking, Ensemble Learning
Subjects:	T Technology > T Technology (General) > T174 Technological forecasting T Technology > T Technology (General) > T57.5 Data Processing
Divisions:	Interdisciplinary School of Management and Technology (SIMT) > 61101-Master of Technology Management (MMT)
Depositing User:	Erlina Komaruljannah
Date Deposited:	24 Aug 2023 06:42
Last Modified:	24 Aug 2023 06:42
URI:	http://repository.its.ac.id/id/eprint/104851

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