Implementasi Metode Support Vector Machine (Svm) Untuk Klasifikasi Rumah Layak Huni (Studi Kasus: Desa Kidal Kecamatan Tumpang Kabupaten Malang) - Weni Agustina; Muhammad Tanzil Furqon; Bayu Rahayudi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
House is an important part in the aspect of life. A habitable house that is good to be used is clean, safe, and comfortable. Lack of knowledge about the function of house in the society, more difficult to imply the realization of a habitable house. Government gets difficulty in assessing the habitable house. In fact, the unpreety house has high income. Government's assistance often misplaced, many people complain because of this. To overcome the problem, then the government needs a system that classifies habitable house and inhabitable house. The system for classification of habitable house was made using the Support Vector Machine (SVM) method. this study uses 160 data that is divided into two types that are habitable and inhabitable. The method used is Support Vector Machine (SVM) method is a good classification method. Support Vector Machine (SVM) method is linear, but SVM method can also be used to solve non-linear problem. The experiment result shows an average accuracy of 98,75% using K-fold Cross Validation test method with k = 10, and SVM method parameters are = 0,5, = 0,001, = 1, = 2, maximum iteration = 10 iteration and using the Polynomial of degree kernel.