Analisis Sentimen Ulasan Pelanggan Kober Mie Setan menggunakan Algoritma Support Vector Machine - Satrio Arif Budiman; Nanang Yudi Setiawan; Dwija Wisnu Brata (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2023)
Kober Mie Setan is the first restaurant to offer spicy noodle products at affordable prices since 2010. New competitors in the same industry make Kober Mie Setan require a business strategy that can maintain a competitive advantage and serve as a reference for evaluation. One way to develop an effective business strategy is to pay attention to customer reviews. However, Kober Mie Setan needs the technology to manage customer reviews that can generate useful information. One solution is to use sentiment analysis of Kober Mie Setan Malang's customer reviews. This study uses 2,496 customer review data from 2016-2022 obtained through web scraping techniques on the Google Review website. Furthermore, the sentiment classification process uses a support vector machine (SVM) algorithm with the term frequency-inverse document frequency (TF-IDF). Testing with the confusion matrix produces an accuracy value of 92%, a reference for model performance because the data is balanced. The results of the sentiment analysis process are visualized in the form of a dashboard and analyzed using root cause analysis. Root cause analysis produces a finding in the form of root causes in food quality, service quality, price, and physical environment, which will be discussed with stakeholders to create business recommendations.