Analysis of Customer Perception on Google Maps Reviews of Klinik Kopi Samarinda Using Extreme Gradient Boosting (Xgboost) - M.Ariya Parengrengi; Heny Pratiwi; Muhammad Fahmi (Politeknik Pertanian Negeri Samarinda, 2026)
This study aims to analyze customer perceptions based on Google Maps reviews of Klinik Kopi Samarinda using the Extreme Gradient Boosting (XGBoost) method. Online customer reviews have become an important source of information for evaluating service quality and customer satisfaction in the food and beverage industry. The data used in this study were collected from Google Maps reviews, consisting of customer comments and ratings. Text preprocessing was conducted through case folding, tokenization, stopword removal, and stemming to prepare the data for analysis. Sentiment labels were classified into positive, negative, and neutral categories. The XGBoost algorithm was applied to perform sentiment classification due to its high performance in handling structured and unstructured data. The results show that the XGBoost model achieved high accuracy in classifying customer sentiment, indicating that most customers have positive perceptions of Klinik Kopi Samarinda. This study demonstrates that machine learning-based sentiment analysis can provide valuable insights for business owners in understanding customer feedback and improving service quality.