IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG - Favorisen Rossyking Lumbanraja; Reza Aji Saputra; Kurnia Muludi; Astria Hijriani; Akmal Junaidi (Jurnal Pepadun, 2021)
Machine Learning has been widely used in terms of predictions for analyzing datasets. One method of Machine Learning is Support Vector Machine (SVM). The house has an important role in the survival of human beings. With the times, many developers are competing to build housing. The purpose of this study is to predicted the housing cost using Support Vector Machine. The data in this research used the data of house in Bandar lampung, the price, the location and the building specification. The amount of data used 51 datas and 33 variables with regression and classification, also used 3 kernels and it's model, 12 times first trial and next 6 experiments done with fitur selection. The trial result was kernel regression polynomial model reached the highest R 2 that was 95,99% linear kernel and gaussian kernel reached R 2 90,99% and 81,43% each. While in accuration classification model trial is obtained in 8 class of gaussian kernel as big as 91,18%, and linear kernel and polynimonal kernel get an accuracy of 90,20% and 89,90%.