Penerapan SVM untuk Klasifikasi Sentimen pada Review Comment Berbahasa Indonesia di Online Shop - Yoshua Refo; Silvia Rostianingsih; Liliana Liliana (Universitas Kristen Petra, 2022)
With so many users accessing online shops, comments are an important aspect when shopping. Buyers can provide comments about the goods or thing that have been purchased, both negative comments and positive comments. By collecting various kinds of comments, the data can be used to classify comments. This research will use the Support Vector Machine (SVM) algorithm which is considered the right method for text classification. The method will be tested for its performance, seen from how good and accurate the method used in classifying comments is. In addition, this research also uses kernels, namely Linear kernels, Radial Basis Function (RBF) kernels, and Polynomial kernels as test scenarios. Based on the test results shown, SVM is a good method in classifying text. SVM classifies text that has gone through the preprocessing stage with an accuracy value of 88% on the RBF kernel, 87% on the linear kernel, and 87% on the polynomial kernel. The accuracy value in the aspect classification itself is 78% on the RBF kernel, 78% on the Linear kernel and 74% on the Polynomial kernel.