Penerapan Algoritma XGBoost Dalam Prediksi Harga Sewa Kos Di Kota Samarinda
-
Amalia Rahman
*
STMIK Widya Cipta Dharma,
Indonesia
- Amelia Yusnita STMIK Widya Cipta Dharma, Indonesia
- Hanifah Ekawati STMIK Widya Cipta Dharma, Indonesia
Abstract
The population growth and increasing economic activity in Samarinda City have led to a rising demand for temporary housing such as boarding houses. However, rental price determination is still largely based on the owner’s intuition rather than objective factors such as available facilities, room specifications, transportation accessibility, and proximity to public amenities. This study aims to develop a rental price prediction model for boarding houses using the Extreme Gradient Boosting (XGBoost) algorithm with a Knowledge Discovery in Database (KDD) approach. The research data were collected through a web scraping process from the Mamikos platform, yielding 231 initial records, which were then cleaned and filtered for outliers, resulting in 225 valid data points. Five main features derived from feature engineering were utilized in the model, namely Facility Score, Combined Specification Score, Nearest Place Score, Transportation Score, and Rental System Score. The evaluation results show that the XGBoost model achieved a Mean Absolute Error (MAE) of Rp348,822, a Root Mean Squared Error (RMSE) of Rp416,139, and a coefficient of determination (R²) of 0.612. These values indicate that the model can explain 61.2% of the variation in rental prices with reasonably good predictive performance. The feature importance analysis reveals that Facility Score and Combined Specification Score are the most influential factors affecting rental prices, while transportation and rental system factors contribute less significantly. This study is expected to serve as a reference for boarding house owners, tenants, and policymakers in determining more objective and competitive rental prices based on a data mining approach.
References
BPS Kota Samarinda, Statistik Daerah Kota Samarinda 2024, Samarinda: Badan Pusat Statistik, 2024.
E. Santoso and D. Ramadhan, “Analisis Kebutuhan Hunian di Kota Samarinda Menggunakan Data BPS,” Jurnal Ekonomi Wilayah, vol. 5, no. 2, 2023.
M. Fadli and S. Kurniawan, “Perbandingan Metode Penentuan Harga Kos Berdasarkan Fasilitas dan Lokasi,” Jurnal Sosio Informatika, vol. 2, no. 1, 2022.
R. Wibisono et al., “Analisis Fasilitas terhadap Harga Hunian di Kota Bandung,” Jurnal Sistem Cerdas, vol. 3, no. 1, 2021.
T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proc. 22nd ACM SIGKDD, 2016.
M. Albar and I. Siregar, “Evaluasi Model Gradient Boosting dalam Prediksi Properti,” Jurnal Teknologi dan Sistem Cerdas, vol. 4, no. 3, 2023.
R. Sitorus and N. Pane, “Implementasi Extreme Gradient Boosting untuk Prediksi Harga Rumah di Kota Medan,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 2, 2024.
B. W. Sari and D. Prabowo, “Analisis Perbandingan Prediksi Harga Rumah dengan Random Forest, Gradient Boosting, dan XGBoost,” Jurnal Intellect, vol. 4, no. 1, 2025.
Y. F. Munawar and A. Arisal, “Analisis Prediksi Harga Sewa Ruko Menggunakan Pendekatan Machine Learning,” RIGGS Journal, vol. 4, no. 3, 2025.
D. N. Mali, M. Danianti, and N. A. Tinus, “Analisis Akurasi Algoritma XGBoost untuk Prediksi Harga Properti Menggunakan Data Geospasial,” Jurnal Teknologi Informasi dan Komputer (JTIK), vol. 5, no. 2, pp. 87–95, 2024.
M. N. Hibatulloh and G. D. Prakoso, “Prediksi Harga Rumah di Bandung Menggunakan Ensemble Learning,” JPIT Informatika, vol. 10, no. 2, 2025.
D. Aqsha, “Perbandingan Kinerja Algoritma XGBoost dan Random Forest untuk Prediksi Harga Rumah di Jabodetabek,” Jurnal Teknologi Informasi UNTAR, 2024.
W. Kurniawan and U. Indahyanti, “Prediksi Angka Harapan Hidup Penduduk Menggunakan Metode XGBoost,” IJAT Journal, vol. 8, no. 1, 2024.
F. Riyadi and E. Wahyudi, “Optimasi Model Prediksi Properti dengan Variabel Spasial,” IJTI Journal, vol. 5, no. 1, 2024.
M. Alfian et al., “Integrasi Data Geospasial dalam Prediksi Harga Properti,” Jurnal Informatika Unhas, vol. 3, no. 2, 2022.
H. Damanik and I. Kurniasari, “Pemodelan Harga Rumah Menggunakan XGBoost,” Jurnal Teknologi Informasi Unair, vol. 4, no. 2, 2024.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37–54.
N. Siregar & D. Fitriani, “Implementasi Algoritma XGBoost untuk Prediksi Harga Properti,” Jurnal Ilmu Komputer dan Informatika (JIKI), vol. 5, no. 3,2024.
A. Nasution & M. Widodo, “Evaluasi Model Prediksi Harga Properti Menggunakan MAE, RMSE, dan R²,” Jurnal Sistem Informasi dan Teknologi Cerdas (JSITC), vol. 4, no. 1, 2024.
L. Adityo & H. Mulyani, “Metodologi Penelitian Data Mining untuk Prediksi Properti,” Jurnal Teknologi Informasi dan Komputer, vol. 6, no. 2, 2023.
Copyright (c) 2025 Amalia Rahman, Amelia Yusnita, Hanifah Ekawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
.png)
.png)
