Sentiment Classification of Features in e-Health Adoption: A Technical and Security Approach Using Convolutional Neural Network (CNN)

Indonesia ranks third globally in e-Health utilization, which is carried out by 57% of respondents. It shows that public awareness of the importance of technology in the health sector has changed significantly. This awareness needs to be continuously enhanced. However, this is not in line with the use of health services for pregnant women. This is related to the factor of adopting health technology that is in line with the features of e-Health itself. This research examines sentiment analysis on 17 e-Health in Indonesia to gain in-depth insight into user perceptions and responses related to features, especially Technical Factors and Security/Privacy Factors taken through the Google Playstore. The methods include sentiment labelling with TextBlob and modelling using Convolutional Neural Network (CNN). The overall model produced an accuracy of 71.67%, indicating that the model is most reliable in classifying aspects of Perceived Ease of Use and Performance Risk. This research is expected to provide valuable insights for application developers and the government in optimizing the quality and trust in e-Health in Indonesia.