Analisis sentimen terhadap pelayanan Kesehatan berdasarkan ulasan Google Maps menggunakan BERT

Abstract

The utilization of technology has developed in various scientific fields, without exception in health. Hospitals, health centers, and clinics are part of the health sector. Thus, it must evolve according to health service standards and patient measures or service user satisfaction that needs to be measured using sentiment analysis. The Media to give opinions to Health service providers is Google Maps. However, the anomaly is that the reviews and the given text are sometimes not correlated. Thus, The utilization of sentiment analysis using the scientific branch of artificial intelligence, namely Natural Language Processing (NLP), is an effective way to infer opinions. The research concluded that the BERT indobenchmark/indobert-base-p1 model has good performance to use of Indonesian text classification with a dataset of 4228 data after preprocessing, which at the beginning of the collection process obtained data as much as 4748 data. Split datasets into 3 data, namely training, validation, and test data, with a ratio of 70:30:30. The experimental results, The researchers found that the model allows the use of the model with other Indonesian texts. The results are 0.85 for accuracy and weighted avg, and macro avg 0.75 on the validation data training process. While the testing data training process is 0.86 for accuracy and weighted avg, the macro avg 0.73. In addition, researchers found that services are the most frequent topic in Health Services. Even though health services have improved, positive sentiment is the highest compared to other sentiment classes.