Naive Bayes Classifier untuk Analisis Sentimen Ulasan Pelanggan pada Domo Coffee and Resto

Puji Hartini; Nana Suarna; Willy Prihartono

doi:10.36499/jinrpl.v6i1.10315

Naive Bayes Classifier untuk Analisis Sentimen Ulasan Pelanggan pada Domo Coffee and Resto

Puji Hartini, Nana Suarna, Willy Prihartono

Abstract

Domo Coffee and resto is one of the well-known cafes located on Jl. DR Sudarsono No.45 Kesambi, Kesambi District, Cirebon City. Domo Coffee and Resto has a variety of food and drinks served and the place is designed to be beautiful and comfortable to visit for various purposes. Of course, there are many kinds of problems related to unsatisfactory service, uncomfortable atmosphere or bad taste of food as well as several other disappointments and dissatisfaction that give rise to negative comments or reviews. Café Domo often receives mixed reviews from customers on the Google review platform. This research aims to analyze the sentiment of customer reviews on Domo Coffee and restaurant and will be completed using the Naïve Bayes Classifier method, namely a classification method based on Bayes' theorem. In this research, based on the author's understanding of sentences regarding sentiment analysis, the author received 374 positive reviews and 58 negative reviews regarding food. 469 positive reviews and 40 negative reviews regarding the atmosphere and 253 positive reviews and 99 negative reviews regarding the service. The highest number of positive comments was obtained by the atmosphere aspect with 469 reviews and the highest negative comments were obtained by the service aspect with 99 reviews. In testing the split data values of 0.8 and 0.2, the highest accuracy was obtained by the service technician with an accuracy of 98.22%, precision of 97.58%, recall of 100% and an F1-score value of 98.78%. The results of this research provide in-depth insight into customers' views of Domo cafe. Cafe owners and stakeholders can use these findings to understand aspects that need to be improved or improved.

Keywords

Sentiment Analysis, Google Review, Naïve Bayes

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DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10315