Comparison of Naive Bayes Method with Support Vector Machine in Helpdesk Ticket Classification

Abstract

The technical support department or helpdesk department is a unit that requires a quick response in handling its tasks. The company's helpdesk team can consist of several individuals who know specific or specialized issues. Typically, technical problems are handled with an application that can track issues based on tickets. Ticket queue systems are used to facilitate control over the actions of the service or repair provided by the team. Helpdesk applications assist in addressing issues reported by users and then help upper-level management distribute tasks and monitor the helpdesk team's performance, including providing solutions to users' various problems. This research aims to predict the placement of fields that serve assistance based on the corpus users provide in the natural language. Prediction modelling is done using the Naïve Bayes and Support Vector Machine algorithms. The modelling results show that the accuracy rate of helpdesk service prediction with the Naïve Bayes algorithm reaches 82.06%, while the accuracy rate of prediction with the Support Vector Machine algorithm reaches 85.30%.

Keywords

• helpdesk
• machine learning
• naïve bayes
• support vector machine
• text mining