Mendeteksi Cyberhate pada Twitter Menggunakan Text Classification dan Crowdsourced Labeling
Abstract
During the 2019 presidential election campaign in Indonesia, a lot of support was made by the community with various forms of support, such as poster distribution or even content on social media. For example, in social media such as Twitter, there were many support tags during the presidential election, such as #2019gantipresiden, #2019tetapjokowi, and other hashtags related to the Indonesian presidential election. However, many hate speeches are contained in tweets with the related hashtag. Hate speech on the internet (cyberhate) could cause disputes between support groups of the two presidential candidates which cause conflicts such as riots and other actions that harm the country. This study uses the SVM algorithm to detect cyberhate that produces the best accuracy of 97%. Also, this study applies crowdsourced labeling in dataset labeling which results in 98% valid data.
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