Hate speech is any form of communication that
attacks a person or group based on attributes such as race,
religion, ethnicity, gender, sexual orientation, nationality, or
other attributes. It can be verbal, written, or nonverbal. Hate
speech can be dangerous because it can lead to violence,
discrimination, and social exclusion for certain demographics of
people. Social media platforms have become the main platforms
for spreading hate speech. Social media companies can combat
and minimize the spread of hate speech by educating their users
about the adverse effects of hate speech and developing a system
to detect, identify, and remove contents that contain hate speech.
Convolutional neural network (CNN) is an algorithm that can
be used to determine whether a tweet is hate speech or not. The
proposed method could facilitate the identification and
detection of hate speech on social media platforms. Feature
extraction is performed using TF-IDF, with FastText used as
feature expansion. In this study, there are three test scenarios
that were applied, where we included baselines with TF-IDF,
FastText implementation, and the best hyperparameter search.
The results showed a significant improvement in accuracy, with
the third scenario achieving an accuracy of 86.87%, an increase
of about 9.84% compared to the results in the first scenario,
which got a result of 77.03%, and the second scenario, which got
a result of 85.03%.