Implementasi Metode Support Vector Machine Dengan Query Expansion Pada Klasifikasi Review Di Situs Traveloka - Meutya Choirunnisa; Nurul Hidayat; Edy Santoso (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2021)
Choosing a tourist destination when you want to go on vacation is a must for people so that they don't choose a tourist location wrong and so they don't get disappointed when they have visited a tour. A review is needed in this case, because with the review the public can find out the comments given by previous visitors. The comments given are not only in the form of praise, but sometimes there are visitors who feel disappointed so that they give bad comments too. The number of comments that sometimes makes people difficult and takes a long time to find out all the advantages and disadvantages of a tourist destination. To overcome this problem, a classification of tourism reviews is carried out using the SVM and QE methods. In this study, 200 data comments were used which were divided into positive and negative. The method used in this research is the Support Vector Machine method with a linear kernel with Query Expansion. QE in this case has the utility to expand the words that are in the test data that have synonyms for words that are not found in the training data. The results of the test produce an average accuracy value of 87.50% with the parameter value of learning rate = 10 and complexity value = 20. Based on the test results, the accuracy of using the SVM method with QE is 87.50% and accuracy using the SVM method without QE of 77.50%.