User Sentiment toward Halodoc Psychological Consultation Services Using RNN and LSTM Analysis - Riyadi Purwanto; Randi Afif; Fajar Mahardika; Dwi Novia Prasetyanti; Cahya Vikasari; Rostika Listyaningrum (Journal of Innovation Information Technology and Application (JINITA), 2026)
In the digital era, online psychological consultation services such as HaloDoc are increasingly used to provide accessible mental health support; however, the rise in users does not always reflect service satisfaction, making sentiment analysis of user reviews essential for understanding public perception. This study aims to classify positive and negative sentiments from Indonesian-language user reviews of HaloDoc’s psychological consultation service and to compare the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms. Using a quantitative approach with text mining and sentiment analysis, data were collected from Google Play Store reviews and processed through case folding, filtering, tokenizing, and stemming, then manually labeled and split into 80% training and 20% testing data. Models were developed using Keras with RNN and LSTM architectures and evaluated using confusion matrix metrics, including accuracy, precision, recall, and F1-score. The results show that RNN achieved 95% accuracy, while LSTM reached 91%; although RNN performed better in accuracy, LSTM demonstrated more stable performance and superior capability in capturing complex contextual information, particularly in longer reviews with varied emotional expressions. Overall, the findings indicate that LSTM is more effective for Indonesian-language sentiment classification in the digital psychology domain and can serve as a foundation for developing automated systems to assess user satisfaction with online psychological services in Indonesia