Opinion Mining of Pedometer Application Reviews on Google Play Store Using Fine-Tuned IndoBERT-Base - Primono, Anggi; Sanjaya, Ucta Pradema (Politeknik Negeri Batam, 2026)
User reviews on the Google Play Store provide valuable insights into user satisfaction and application performance. However, manual analysis of these reviews is inefficient due to large data volume and the informal characteristics of the Indonesian language. This study proposes an opinion mining approach using a fine-tuned IndoBERT-Base model to classify user sentiments into three classes: positive, neutral, and negative. A total of 1,665 reviews of a Pedometer application were collected, with 1,636 reviews retained after preprocessing. The dataset was divided into training, validation, and test sets using stratified sampling to preserve class distribution. Experimental results show that the proposed model achieves an accuracy of 94.51% and a weighted F1-score of 0.93 on the test set. Despite strong overall performance, the results indicate that class imbalance significantly affects the classification of neutral and negative sentiments. Error analysis reveals that ambiguous expressions and limited samples in minority classes remain challenging for the model. This study demonstrates that fine-tuned IndoBERT-Base is effective for sentiment analysis of Indonesian mobile application reviews while highlighting the importance of addressing imbalanced data in opinion mining tasks.