House Price Prediction Using the Random Forest Algorithm on the Rapidminer Application

Fitria Fitria; Muhammad Syahid Pebriadi

doi:10.55927/fjst.v4i2.20

Authors

Fitria Fitria Politeknik Negeri Banjarmasin
Muhammad Syahid Pebriadi Politeknik Negeri Banjarmasin

DOI:

https://doi.org/10.55927/fjst.v4i2.20

Keywords:

Data Mining, Prediction, Random Forest, Rapidminer

Abstract

Home price prediction is an important aspect of the property industry, especially for real estate agents and potential buyers. With the advancement of technology, machine learning is used to improve the accuracy of home price estimates. One of the algorithms that is often used is Random Forest, which is able to capture complex patterns in data. However, previous research has shown that although this model has high accuracy (high R²), prediction errors are still significant (RMSE and MAE are high), indicating features that have not been fully modeled well. This research uses property datasets, preprocessing (normalization and feature selection), and applies Random Forest in RapidMiner. The model was evaluated using RMSE, MAE, and R², which showed R² = 0.78, but with RMSE = 4,757,343 and MAE = 3,200,000, indicating a large prediction difference. The most influential features are the area of the building (40%), the quality of the house (25%), and the number of bathrooms (10%).

References

Adetunji, A., Funmilola Alaba, A., Ajala, Oyewo, O., Akande, Y., Oluwadara, G., & OLUWATOBI, A. (2022). House Price Prediction using Random Forest Machine Learning Technique. In Procedia Computer Science (Vol. 199). https://doi.org/10.1016/j.procs.2022.01.100

Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Li, X., Zhang, H., Wang, L., Liu, J., Breiman, L., Zhu, W., Li, R., Wang, J., Hofmann, M., Klinkenberg, R., Singh, A., Patel, R., & Gupta, S. (1996). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM, 39(11), 27–34. https://kdd.org/exploration_files/Fayyad1996.pdf

Hiroshi, M. (2019). Yamazaki, H.: Why Mt. Fuji is Located There? ―History of Japanese Islands as Revealed by Geomorphology. Geographical Review of Japan Series A, 92, 405–406. https://doi.org/10.4157/grj.92.405

Hoffman, D. W. (n.d.). Perkembangan Teknologi: Bagaimana Menyikapi Tantangan dan Peluangnya. 154–178. http://jurnal.polimdo.ac.id/index.php/ab/article/view/62/61

Khoiriyah, S., & Fatah, Z. (2024). Penerapan Algoritma Linear Regression dalam Memprediksi Harga Rumah Menggunakan RapidMiner. 3(2), 107–115.

Lathifah, U., & Danar Dana, R. (2024). Implementasi Metode Linear Regression Untuk Prediksi Harga Properti Real Estate Menggunakan Rapidminer. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 1129–1137. https://doi.org/10.36040/jati.v8i1.8919

Ma’mum, S., Pratama, R., Nurkholis Ajie Kurniawan, M., Putra Mulya, A., Ariyadi Anatasia, A., & Fansyuri, M. (2025). Implementasi Algoritma Regression Untuk Prediksi Harga Rumah Di Boston . JRIIN :Jurnal Riset Informatika Dan Inovasi, 2(11 SE-), 2034–2040. https://jurnalmahasiswa.com/index.php/jriin/article/view/2242

Rais, A. N., Warjiyono, W., Alfarobi, I., Hadi, S. W., & Kurniawan, W. (2024). Analisa Prediksi Harga Jual Rumah Menggunakan Algoritma Random Forest Machine Learning. Jurnal Riset Sistem Informasi Dan Teknologi Informasi (JURSISTEKNI), 6(2 SE-Articles). https://doi.org/10.52005/jursistekni.v6i2.323

Swathi, B. (2019). House Price Prediction Analysis using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 7, 1484–1493. https://doi.org/10.22214/ijraset.2019.5251

Wardani, A. P., Irawan, H. A., & Syah, M. P. (2024). Analisis dan Prediksi Harga Properti Rumah di Kota Surabaya dengan Algoritma Random Forest. 2024(September 2023), 885–894.

Warjiyono, Nur Rais, A., Alfarobi, I., Wira Hadi, S., & Kurniawan, W. (2024). Analisa Prediksi Harga Jual Rumah Menggunakan Algoritma Random Forest Machine Learning. JURSISTEKNI (Jurnal Sistem Informasi Dan Teknologi Informasi), 6(2), 416–423.