Identifikasi Ujaran Kebencian Multilabel Pada Teks Twitter Berbahasa Indonesia Menggunakan Convolution Neural Network - Aditya Perwira Joan Dwitama; Syarif Hidayat (STMIK Budi Darma, 2021)
There has been a significant increase in communication activities between internet users in online media due to the increase in social media users. For instance, Twitter users may send messages via their tweets. However, tweets can also contain negative meanings. Therefore, it deserves special attention as it has the potential to contain hate speech. Even the government deems it necessary to publish regulations to deal with hate speech cases such as the Information and Electronic Transactions Law (ITE Law) issued in 2018 Article 28 paragraph 2 of the Hate Speech. Machine Learning (ML) is one of the techniques that can be used in identifying patterns. There are various types of data that ML can be applied to, including text (known as Text Analytic). Previous research has used the Support Vector Machine (SVM) method to identify hate speech on Twitter text with more than one label (multilabel). The purpose of this study was to identify hate speech on Twitter with a label of more than one (multilabel) via Convolutional Neural Network (CNN). The study obtained the best CNN model with an accuracy of 98.76% from the multi-label dataset on hate speech in Indonesian texts