Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach

The rapid growth of application development has made applications an integral part of people's lives, offering solutions to societal problems. Health service applications have gained popularity due to their convenience in accessing information on diseases, health, and medicine. However, many of these applications disappoint users with limited features, slow response times, and usability challenges. Therefore, this research focuses on developing a sentiment analysis system to assess user satisfaction with health service applications. The study aims to create a sentiment analysis model using reviews from health service applications on the Google Play Store, including Halodoc, Alodokter, and klikdokter. The dataset comprises 9.310 reviews, with 4.950 positive and 4.360 negative reviews. The IndoBERT pre-training method, a transfer learning model, is employed for sentiment analysis, leveraging its superior context representation. The study achieves impressive results with an accuracy score of 96%, precision of 95%, recall of 96%, and an F1-score of 95%. These findings underscore the significance of sentiment analysis in evaluating user satisfaction with health service applications. By utilizing the IndoBERT pre-training method, this research provides valuable insights into the strengths and weaknesses of health service applications on the Google Play Store, contributing to the enhancement of user experiences.

@article{Imaduddin2023,
  title     = {Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Helmi Imaduddin and Fiddin Yusfida A’la and Yusuf Sulistyo Nugroho},
  doi       = {10.14569/IJACSA.2023.0140813},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140813}
}