JBASE - Journal of Business and Audit Information Systems

This study aims to apply Natural Language Processing (NLP) for sentiment analysis of customer reviews for Seblak Bandung Pedas Hot Gang 2, obtained from Google Maps View. In the digital era, customer reviews play a crucial role in determining the reputation of a business, especially in the culinary industry. Sentiment analysis using NLP enables business owners to automatically identify customer opinions without reading each review individually. The methods used in this study include web scraping to collect customer review data, followed by data preprocessing, text feature extraction using TF-IDF and Word Embedding, and sentiment classification using Machine Learning models (Naïve Bayes, SVM, Random Forest). The results show that the sentiment classification model successfully categorizes customer reviews into positive, negative, or neutral. A total of 63 reviews (57.8%) were classified as positive, 34 reviews (31.2%) as negative, and 12 reviews (11.0%) as neutral. The Naïve Bayes model achieved the highest accuracy at 77.27%, followed by SVM (72.73%) and Random Forest (59.09%). From the sentiment analysis results, it is evident that most customers are satisfied with the product and service quality, though there are still criticisms regarding the level of spiciness and the price, which is considered high. By applying NLP, business owners can gain deeper insights into customer sentiments and make more informed decisions to improve service quality.

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