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MS Glow for Men is sponsoring the Gresini Racing Team in the 2022 MotoGP season. The suitability between MS Glow for Men and Gresini is considered low. Meanwhile, several previous studies have revealed that the suitability between a sponsor and the sponsored party determines the effectiveness. Gresini's best performance occurred 20 years ago, accompanied by lower popularity on Instagram compared to 11 other teams. These two conditions contradict the goal of sponsorship as a marketing communication tool that requires high visibility. The attractiveness of this sponsorship is also due to its timing in the middle of the season. Based on these factors, this research aims to obtain a comprehensive overview of the reasons and strategies behind this sponsorship. Using a qualitative approach and a case study method, data was gathered through interviews, observations, and documentation studies. The research successfully found that the reasons for sponsorship include the impact of top-level effects, consumer research, external discussions, exposure, connections, values, cooperation, and riders. Sponsorship is conducted to enhance brand awareness, maintain brand image, and expand geographical reach. Focusing on men aged 20-35 who are automotive and MotoGP enthusiasts, MS Glow for Men also extends its sponsorship into six derivative programs, not merely relying on logo placement on the Gresini livery.

The MotoGP One race in West Nusa Tenggara Lombok, Mandalika which was held on March 18 2022, received many responses or reactions from the public on social media, especially Twitter. There are those who agree and disagree about the holding of MotoGP in Mandalika, to find out the responses of the people who agree or disagree is needed that can process tweets data using the sentiment analysis method. The use of BERT (Bidirectional Encoder Representations from Transformers) for sentiment analysis produces a bidirectional language model that can understand the context of all words from a sentence. The dataset used goes through preprocessing stages such as case folding, data cleaning, tokenization, normalization, and removal of stopwords before sentiment analysis is carried out. This study uses several hyperparameters, namely a batch size of 32, the optimizer uses Adam with a learning rate of 3e-6 or 0.000003, and an epoch of 25. The evaluation results of the model obtain an accuracy of 55%. Precision for positive by 56%, neutral by 59%, and negative by 44%. Recall for positive is 74%, neutral is 29%, and negative is 54%. F1-score for positive is 64%, neutral is 38%, and negative is 48%.