Perbandingan Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Google Maps Pusat Perbelanjaan - Eliza Cahyaningrum; Astrid Novita Putri (Forum Kerjasama Pendidikan Tinggi, 2026)
The rapid growth of user reviews on Google Maps is not always accompanied by ease in understanding the sentiment contained within them, causing tourists and the general public to face difficulties in determining shopping centers with good reputation and service quality. The lack of information regarding visitor satisfaction levels, along with various facility-related issues such as crowd density, limited parking space, and the comfort of public facilities, combined with the large number of subjective and unstructured reviews, makes manual sentiment analysis ineffective and potentially leads to less accurate conclusions. This investigation aims to analyze sentiment from Google Maps reviews of shopping centers in the city of Semarang utilizing the Support Vector Machine (SVM) and Naïve Bayes methods. The data were collected from five shopping centers with the highest number of reviews in Semarang, namely Paragon Mall, Mall Ciputra, Java Mall, DP Mall, and Queen City Mall. The investigation method includes text preprocessing, TF-IDF weighting, and sentiment classification into three classes: negative, neutral, and positive. The dataset was divided into training and testing data with a ratio of 80:20. The outcomes reveal that the Naïve Bayes method achieved an accuracy of 85.56%, while the Support Vector Machine (SVM) method achieved an accuracy of 89.20%. Considering the outcomes, the SVM method performs better in classifying sentiment from Google Maps reviews of shopping centers in Semarang.