Klasifikasi Customer Intent untuk Mengetahui Tingkat Kepuasan Pelanggan menggunakan Metode Support Vector Machine pada Restoran Bakso President - Julia Ferlin; Fitra Abdurrachman Bachtiar; Alfi Nur Rusydi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
The number of Bakso restaurants in Malang require Bakso President to improve business strategies to maintain or increase customer satisfaction. Intent classification is one way to assist Bakso President in categorizing customer intent as supporting information in generating business strategy or decisions. Customer intent itself is the desire or intention of the customer on a certain product or service. This study classifies customer intent based on 2252 reviews data written by customers on the TripAdvisor and Google Review sites then classified according to 3 categories of customer intent, namely quit, direct, complaint intent, and "other" category. This research performs by Support Vector Machine algorithm to classify the data and TF-IDF as the term weighting algorithm. This data model establishes that intent classification can be performed with 80% Accuracy, 66% average Precision value, 43.7% Recall, and 47% F1-score. In order to visualize the classification's result, the dashboard is used and then it will be evaluated by the SUS (System Usability Scale) questionnaire. The evaluation performs by Manager of the Bakso President restaurant himself and resulted in 75 usability score. This value implies that the dashboard has "Excellent" usability and received very favorably by the user.