<![CDATA[Hate speech is a form of abusive language and toxic language aimed at individuals, groups, races or ethnicities, genders and certain religions. The problem that occurs is the absence of a good filtering process on social media. For this reason, a filtering process is needed that aims to distinguish between video content that contains hate speech or not. The filtering process can use a classification model. This classification model uses an artificial intelligence algorithm. The purpose of this study is to conduct a literature review on algorithms that can be proposed for detecting hate speech videos. The algorithm used refers to the concept of green technology, with low energy resource consumption and minimizing negative environmental impacts. The literature study conducted obtained the CNN, BERT and LSTM algorithms for the hate speech video classification model. The three algorithms can be used as a reference to obtain a Green AI model by considering the low performance indicator parameters on the CPU, or using a fusion level that can reduce CPU performance. This is in accordance with the concept of green technology, reducing the use of computing processes that absorb large amounts of electrical energy.]]>
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