Sentiment Analysis of Joglo Wifi UMKM Service with Naive Bayes Method

Abstract

In today's digital era, many businesses face challenges in managing and responding to customer feedback and suggestions contained in comments. Joglo Wifi Corner UMKM Business Center & Coffee experiences similar difficulties, where the lack of customer feedback management negatively impacts service quality and customer satisfaction. In this study, we aim to develop and implement a sentiment analysis system that is able to classify customer sentiment into positive, negative, or neutral. The process begins with data collection. customer comments from the official Joglo Wifi Corner website, which are then processed through a preprocessing stage including tokenization, stop word removal, and lemmatization. Then, a Logistic Regression Naive Bayes model is drilled using the processed data to classify sentiment. The evaluation results show that this system achieves an accuracy of 91.67%, with an average precision of 0.646, an average recall of 0.805, and an average F1-Score of 0.701. The implementation of this system provides valuable insights for Joglo Wifi Corner managers in making strategic decisions to improve service quality. By responding more effectively to customer feedback, Joglo Wifi Corner promises that this research can improve customer satisfaction and expand the company's market coverage and identify areas for improvement based on customer feedback, thereby strengthening customer relationships. This approach also uses Agile Development system development methodology to ensure iterative and responsive development to changing user needs.