IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM PREDIKSI HARGA RUMAH

Pio Pratama, I Wayan

Jurnal Akademisi Vokasi
Vol 2 No 2 (2023): Jurnal Akademisi Vokasi

This study aims to predict house prices in the United States based on features such as Avg. Area Income, Avg. Area House Age, Avg. Number of Rooms, and Area Population. The dataset used consists of several thousand entries without missing values. Through initial data exploration, a significant correlation was found between several features and house prices. Outliers were analyzed and considered in the modeling process. The Support Vector Machine (SVM) algorithm with various kernels was applied, where the Radial Basis Function (RBF) kernel showed the best performance, explaining about 70.78% of the variation in house prices. The results of this study highlight the potential of the SVM algorithm in property price prediction and provide insights for further property analysis.