Analysis of Public Perceptions of Tertiary Hospitals Through Google Maps Using Naïve Bayes and Wordcloud - Wardani, Vira Aulia Kusuma; Pertiwi, Arlette Suzy Puspa; Suwardhani, Achmad Dheni (CV. ULIL ALBAB CORP, 2025)
The study investigates the public perception of a tertiary hospital through Google Maps digital reviews using a Naïve Bayes approach and wordcloud. The data was collected by manual scraping technique from 2024-2025 and processed through preprocessing stages including cleansing, case folding, stopword removal, tokenizing, dan filter by legth. The data was then weighted by the TF-IDF technique before sentiment modeling by Naïve Bayes Classification. All of the processes use the Rapid Miner application. From 830 reviews collected, the accuracy of the Naïve Bayes model is 72,29% with precision 78,94%, recall 72,28%, and F1 score 74,76%. The results show that the dominant reviews are positive and describe the hospital health service as excellent. Therefore, the negative review shows that the hospital needs to improve its management in the pharmacy sector, administration, and patient queue. Neutral reviews tend to be descriptive without showing strong sentiments.