Sentiment Analysis to Evaluate Public Service Perception among Surakarta City Residents Using the BiLSTM Model - setiawan, very dwi; Dwi Utai Iswavigra (Universitas Medan Area, 2025)
The growing use of social media as a platform for public communication has opened new opportunities for understanding public opinion regarding government policies, including public services. One of the cities actively discussed on social media is Surakarta, where citizens openly express both appreciation and criticism of local government performance. This study aims to analyze public sentiment toward public services in Surakarta by employing a deep learning-based sentiment analysis approach, specifically using the Bidirectional Long Short-Term Memory (BiLSTM) model. Data were collected from Twitter/X using a web crawling technique with the keywords “pemerintah solo” (Solo government), “kota Surakarta” (Surakarta city), and “kota solo” (Solo city), resulting in 2,168 tweets. The analysis process involved several stages, including preprocessing, sentiment labeling using a lexicon-based method, feature representation with Word2Vec, and classification using five models: SVM, Random Forest, CNN, LSTM, and BiLSTM. The evaluation results show that BiLSTM achieved the best performance with an accuracy of 90.21%, precision of 91.05%, recall of 89.84%, and F1-score of 90.43%. The conclusion of this study is that BiLSTM can effectively classify public sentiment toward public services, especially in the context of informal social media texts. The implication of this research indicates that sentiment analysis can serve as a decision-support tool for designing more responsive and data-driven public policies and provide strategic insights for local governments in improving the quality of public services.