Analysis Of Community Sentiment Towards Skm At The Public Service Mall (Mpp) Medan City Using The Naive Bayes Method

Abstract

Sentiment analysis is an important method in understanding public perceptions and opinions towards public services. This research aims to analyze public sentiment towards the Community Satisfaction Survey (SKM) at the Public Service Mall in Medan using the Naive Bayes method. The data used in this research was obtained from the results of a community satisfaction survey conducted by the Medan City MPP Implementer from January to June 2024. The Naive Bayes method was chosen because of its ability to carry out text classification with fairly good accuracy even with limited training data. The analysis process includes data collection, text pre-processing, feature extraction, and sentiment classification into three main categories: positive, negative, and neutral. The research results show that the majority of people have positive sentiments towards the licensing services provided, however there are several aspects that still need to be improved to increase overall community satisfaction.