House Price Prediction in Bantul Regency Using Support Vector Regression Algorithm

  • Windha Mega P Dhuhita Universitas Amikom Yogyakarta
Keywords: SVR, Prediction, RBF, RMSE

Abstract

House is a dwelling place that is necessary for the survival of people as a basic need. People spend at least half their day at home, such as for eating, bathing, sleeping or just relaxing with family members. The price of a house is influenced by the specifications of the house, such as location, land area, building area, number of bedrooms, number of bathrooms and also number of floors. These variables will affect the determination of the house price.Prediction model was created to estimate the house price from these variables. This study uses the Support Vector Regression algorithm with testing using Linear, RBF and Polynomial kernel functions to predict house prices. The data source for this study was obtained from rumah123.com. The model evaluation of the prediction results used RMSE, R2 and MAPE techniques.The number of data used for this study was 1617 data after preprocessing. The best result of this SVR algorithm was obtained with the RBF kernel function with an RMSE error value of 11.71%.

References

[1] S. Keman, “Kesehatan Perumahan dan Lingkungan Pemukiman,” Jurnal Kesehatan Lingkungan Unair, vol. 2, no. 1, 2005.
[2] A. Saiful, S. Andryana, and A. Gunaryati, “Prediksi Harga Rumah Menggunakan Web Scrapping Dan Machine Learning Dengan Algoritma Linear Regression,” 2021, [Online]. Available: http://jurnal.mdp.ac.id
[3] D. Sitanggang, “MODEL PREDIKSI OBESITAS DENGAN MENGGUNAKAN SUPPORT VECTOR MACHINE,” Jurnal Sistem Informasi dan Ilmu Komputer Prima), vol. 5, no. 2, 2022.
[4] R. E. Cahyono and J. P. Sugiono, “Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen (Performance Analysis of Support Vector Regression (SVR) Methods in Predicting the Consumer Price Index),” vol. 1, no. 2, pp. 106–116, 2019, [Online]. Available: www.siskaperbapo.com
[5] M. R. Pahlawan, A. Djunaidy, and R. A. Vinarti, “Prediksi Indeks Harga Saham Menggunakan Model Hibrida Recurrent Neural Network Dan Genetic Algorithm,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 4, 2022, [Online]. Available: http://jurnal.
[6] C. Bella, Y. Rizola Pratama, and P. Studi Sistem Informasi Fakultas Sains dan Teknologi, “Prediksi Jumlah Tunggakan Pajak Jatuh Tempo Menggunakan Algoritma Support Vector Regression,” 2019.
[7] P. D. Kusuma, Machine Learning Teori, Program, Dan Studi Kasus. Deepublish, 2020. [Online]. Available: https://books.google.co.id/books?id=4k3sDwAAQBAJ
[8] Z. Rais, “ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN KERNEL RADIAL BASIS FUNCTION (RBF) UNTUK MEMPREDIKSI LAJU INFLASI DI INDONESIA,” VARIANSI: Journal of Statistics and Its Application on Teaching and Research, vol. 4, no. 1, pp. 30–38, 2022, doi: 10.35580/variansiunm13.
[9] A. W. Ishlah, S. Sudarno, and P. Kartikasari, “IMPLEMENTASI GRIDSEARCHCV PADA SUPPORT VECTOR REGRESSION (SVR) UNTUK PERAMALAN HARGA SAHAM,” Jurnal Gaussian, vol. 12, no. 2, pp. 276–286, Jul. 2023, doi: 10.14710/j.gauss.12.2.276-286.
[10] Miftahuddin, Ananda P, and Ichsan S, “Analisis Hubungan Antara Kelembaban Relatif Dengan Beberapa Variabel Iklim Dengan Pendekatan Korelasi Pearson di Samudera Hindia,” 2021. doi: http://dx.doi.org/10.23960%2Fjsm.v2i1.2753.
[11] Muhammad Ariqleesta Hidayat, “Pengenalan tentang GridSearchCV di Python,” 2021. https://algotech.netlify.app/blog/gridsearchcv/
[12] A. R. Wijaya, “MODEL PREDIKSI DATA HARGA MINYAK MENTAH DUNIA DENGAN METODE EXPONENTIAL SMOOTHING,” 2023.
Published
2024-06-10