<![CDATA[Previous studies in the context of sentiment analysis have been conducted in various types of text domains, including product reviews, movies, news, and opinions. Sentiment analysis focuses on recognizing the valence of positive or negative orientations. In sentiment analysis, something that is less explored but is needed in analysis is the recognition of types of emotions / classification of emotions. This research has contributed to knowledge of the applicationof distilBERT to Indonesian Language data which is still relatively new and the success of fine-tuning and hyperparameter-tuning that has been applied to distilBERT with various comparison methods. Emotion classification can help provide the right decisions in a company, government, and marketing strategy. Emotion classification is part of sentiment analysis which has more detailed and in-depth emotion labels. From these various urgencies, this research builds and fine-tuning distilBERT model to help classify emotions from an Indonesian sentence. Emotion classification in Indonesian language data using the distilBERT model is still relatively new. This research consists of 4 steps: data collection, data preprocessing, hyperparameter-tuning and fine-tuning, and comparison of results from various models that have been applied in this research: BERT, RoBERTa, and distilBERT. DistilBERT got the highest training accuracy = 94.76, the highest testing accuracy was obtained by distilBERT-frezee = 86.67, and the highest f1-score was obtained by distilBERT Modif = 87. In distilBERT-frezee the main cause of the model getting significant results is the dropout hyperparameter which reduces the f1-score value by 3 if it is not used, and the second cause is the freeze-layer which reduces the f1-score value by 1 if it is not used.]]>
