Text clustering is the task of grouping a set of texts so that text in the same group will be more similar than those from a different group. The process of grouping text manually requires a significant amount of time and labor. Therefore, automation utilizing machine learning is necessary. The standard method used to represent textual data is Term Frequency Inverse Document Frequency (TFIDF). However, TFIDF cannot consider the position and context of a word in a sentence. Bidirectional Encoder Representation from Transformers (BERT) model can produce text representation that incorporates the position and context of a word in a sentence. This research analyzed the performance of the BERT model as data representation for text. Moreover, various feature extraction and normalization methods are also applied for the data representation of the BERT model. To examine the performances of BERT, we use four clustering algorithms, i.e., k-means clustering, eigenspace-based fuzzy c-means, deep embedded clustering, and improved deep embedded clustering. Our simulations show that BERT outperforms the standard TFIDF method in 28 out of 36 metrics. Furthermore, different feature extraction and normalization produced varied performances. The usage of these feature extraction and normalization must be altered depending on the text clustering algorithm used.