Implementation of Cosine Similarity in an Automatic Classifier for Comments

Muhammad Habibi (Universitas Jenderal Achmad Yani Yogyakarta)

JISKa (Jurnal Informatika Sunan Kalijaga)
Vol. 3 No. 2 (2018): September 2018

Classification of text with a large amount is needed to extract the information contained in it. Student comments containing suggestions and criticisms about the lecturer and the lecture process on the learning evaluation system are not well classified, resulting in a difficult assessment process. So from that, we need a classification model that can classify comments automatically into classification categories. The method used is the Cosine Similarity method, which is a method for calculating similarities between two objects expressed in two vectors. The data used in this study were 1,630 comment data with several different categories. The test in this study uses k-fold cross-validation with k = 10. The results showed that the percentage accuracy of the classification model was 80.87%.