Analisis Sentimen Ulasan Restoran Menggunakan Metode Support Vector Machine - Yoel Julianto; Djoni Haryadi Setiabudi; Silvia Rostianingsih (Jurnal Infra, 2022)
Reviews on restaurants on the internet have a huge impact on a restaurant. Reviews provided help other customers to evaluate the business or services provided from a  restaurant. Customers can leave positive or negative reviews. The large number of reviews from customers makes it difficult for restaurants to know if their restaurant has move positive or negative reviews. In this undergraduate thesis an application will be made to determine whether a restaurant has positive or negative reviews.Application that is equipped with text mining features will help restaurant in evaluate their restaurant. The steps taken are preprocessing which consist of case folding, tokenization, stopword removal, and stemming. Then the process of converting text data into vector using TF-IDF. Furthermore the data will be trained using Support Vector Machine which later will generate a model that will be used to make predictions from input data. The data which be used as training are Indonesian-language reviews from various restaurants.From this research conducted the result showed an accuracy of 93% and f1-score of 93%. To increase accuracy and f1-score values, classification model require TF-IDF parameters min_df  0.05, max_df  0.75, norm l2, n-gram (1, 2), linear SVM kernel with C 1. Besides TF-IDF and SVM parameters, the number of datasets can also increase confusion matrix and f1-score values.