SENTIMENT ANALYSIS OF COFFEE SHOP REVIEWS USING RANDOM FOREST CLASSIFIER METHOD - Berliana Nur Isnayni; Nurirwan Saputra; Tri Hastono (CV. Fahr Publishing, 2024)
A Coffee shop is a place that serves drinks made from processed coffee grains, various drinks and various snacks to accompany coffee to consumers. Coffee shop reviews can help owners to find out how the community responds to the coffee shop and its services. The data used in this study was 2000 data taken on the old Google Maps Kopi Ampirono by sraping data using Instant Data Sraper. From the abundance of review data, it takes a long time to fully understand the polarity of positive, negative, and neutral reviews manually. Because of this, an accurate sentiment analysis model is needed to classify customer reviews into positive, negative, or neutral reviews. In this study, sentiment analysis used coffee shop reviews using the Random Forest Classifier method. The Preprocessing stage involves the process of case folding, tokenization, stopword removal and stemming. The results of this study are coffee shop reviews of the Random Forest Classifier method classification with an accuracy rate of 79% and a Precision value of 81%, Recall of 97% and while the F1 Score of 88%.