LSTM and Word Embedding: Classification and Prediction of Puskesmas Reviews Via Twitter

Abstract

Puskesmas is a location for top-notch volunteer work that benefits the village and city governments alike. Therefore, patient feedback regarding the kinds of services offered by the community health center is required in an attempt to improve the quality service performance. Patient opinions can be expressed through reviews or opinions about the quality of patient care on social media sites like Facebook, Instagram, Twitter, WhatsApp, and Instagram. On the other hand, thoughts shared on social media are lengthy, unstructured texts. This complicates text analysis and makes it impossible to compare the caliber of services offered by Puskesmas managers. Furthermore, a number of Community Health Centers lack websites that allow users to rank Community Health Centers according to user interest and visual appeal and efficiency in operations. Thus, the purpose of this study is to classify and present sentiment analysis from Twitter about community health centers' health services. The scope focuses on five factors: administrative services, finances, mechanisms, health worker friendliness and skills, and administrative services. The LSTM word embedding model and the adadelta and adamax optimizers are used in word embedding for text mining. A confusion matrix was used to evaluate the developed model's degree of accuracy in categorizing and forecasting patient reviews. Results from the LSTM and Adamax models with a precision level of 76%, Recall 69% and Accuracy 71%. The results of this research show that the LSTM method and Adamax optimizer can classify and predict public opinion data about Puskesmas services via Twitter quite well. A high level of accuracy is very important to ensure that community opinions can be properly identified by the model, so that it can support the decision-making process in improving the type of Puskesmas services. To improve the model, further studies can be conducted on how to select parameters, select features, and create a quality dataset.