PERBANDINGAN METODE PCA-SVM DAN SVM UNTUK KLASIFIKASI INDEKS KEPUASAN MASYARAKAT TERHADAP LAYANAN PENDIDIKAN DI KABUPATEN JENEPONTO - Nur Ikhwana; Muhammad Nusrang; Sudarmin Sudarmin (VARIANSI: Journal of Statistics and Its application on Teaching and Research, 2021)
Support Vector Machine (SVM) is one of the classification methods used to find the best hyperplane by maximizing the distance between classes. SVM aims to build a model that can predict the given test data. The SVM method can be implemented easily and the testing time is short, but it needs to reduce the computation burden. One way that can be done is to perform feature extraction to get the main characteristics of the data. The method that can be used to extract features is Principal Component Analysis (PCA). PCA is used to reduce the dimensions of data which are generally used in numerical scale data. If the data in the study used categorical data, then the PCA used was Nonlinear PCA. The data used in this study is the Community Satisfaction Survey data in Jeneponto Regency. This study compares the PCA-SVM and SVM methods for the classification of the Jeneponto Regency Community Satisfaction Index. The overall PCA-SVM classification results are better than SVM with 100% accuracy.