Multilabel Classification for Toxic Comments in Indonesian
AbstractThe more rapid development of the internet world, users can make comments on a variety of content on social networks, such as social media, blogs and others. Free users make comments triggering negative comments, making insults and incitement. By classifying user comments it is hoped that the system can be smarter to be able to distinguish threat, insult and incitement comments. The technique for classifying user comments uses deep learning, consisting of 6 classes. The results of experiments that have been conducted show that deep learning models produce an accuracy rate above 98%.
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-Share Alike (CC BY-SA)