Social media is a popular communication and information platform due to its ease and speed of access. By using social media, one can express himself freely. This triggers irresponsible individuals to utter hate speech with the aim of bringing down a person or group of people. Misogyny is a form of hate speech directed at women. The problem of misogyny should not be underestimated because misogyny can be one of the main reasons women feel miserable. In this study, a model will be built to detect misogyny text on the Indonesian language TikTok social media using the IndoBERTweet pre-trained model. IndoBERTweet is a pre-trained model based on the BERT model, which has been trained using Indonesian language datasets taken from the previous Twitter social media, resulting in a good performance for detecting misogynous texts on social media by classifying them. The dataset used is in the form of text data taken from misogyny comments by focusing on forms of misogyny in the form of stereotypes, dominance, sexual harassment, and discredit in short video content on women's TikTok social media accounts. The performance of built model performs hyperparameter settings which include batch size 16, epochs 10, and learning rate 7e-5 and is evaluated using a confusion matrix with the best accuracy results of 76.89%.