Analisis Sentimen terhadap RSUD Salatiga Menggunakan SVM dan TF-IDF

Abstract

The Salatiga Regional General Hospital (RSUD) plays an important role in providing healthcare services. This research analyzes public sentiment towards RSUD Salatiga using the SVM method with a linear kernel for sentiment analysis and TF-IDF for feature extraction. The dataset consists of 414 processed reviews, including case folding, data cleaning, tokenization, normalization, stopword removal, and stemming. Evaluation shows that the model achieved an accuracy of 84.00%; precision of 84.00%; recall of 83.25%; and an F1-score of 83.53%. A total of 55.8% of reviews indicated positive sentiment and 44.2% negative sentiment, highlighting the need for improvements in the queue system, waiting times, and parking facilities. The SVM and TF-IDF methods were chosen for their ability to handle large text data with high accuracy. This research provides practical contributions in the form of recommendations such as the implementation of a technology-based queue system. Limitations include the limited amount of data and platform bias, so exploring other algorithms, such as Naive Bayes and Random Forest, is recommended.