Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine - Wanda Athira Luqyana; Imam Cholissodin; Rizal Setya Perdana (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Instagram is the most popular social media in these recent days. The users who start from kids, teenagers to adults, have the role in boosting the popularity of Instagram. However, this social media could not be seperated from the dangers of cyberbullying which is done often by the users, especially in the comment column. The dangers of cyberbullying are certainly worried many people because of the impact it has. Therefore, a sentiment analysis in Instagram comment column can be done in order to find out the sentiments in each comment. Sentiment analysis is a branch of text mining science which is used to extract, understand, and cultivate the data. This research used Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM) classification method to examine the sentiments in each comment. Data consisted of 400 data which taken offline have a total 1799 features. The comment document is divided into 70% of training data and 30% of test data. Based on the tests performed, the best parameters obtained in the SVM method are the degree of polynomial kernel 2, the average of learning rate of 0.0001, and the maximum number of iterations which is 200 times. From these result, it obtained that the highest accuracy is 90%, 50% in the training data composition and 50% composition of test data.