Ticket Prediction using LSTM on a GLPI System

Abstract

GLPI (Gestionnaire Libre de Parc Informatique) is software for asset management, with an additional interface for submitting requests and reporting incidents to computer technicians in the form of tickets. A ticket is a disruption ticket, also called a problem report, that is used in an organization to track the detection, reporting, and resolution of several problems, The number of incoming and unresolved tickets has an impact on intense uncertainty and instability for customers. The data used is primary data, namely ticket data obtained from the GLPI system in the form of raw data, data is processed and recapitulated based on daily customer request tickets. In this study, using the LSTM (long short-term memory) model with the dataset being eight attributes with a total of 2035 records, for optimal data performance, we use min-max scaling sci-kit learn to transform data, extract features, and create models to predict tickets.  The parameters used include batch_size = 32, epoch = 100, and learning_rate = 0.001, with the optimizer being Adam. The best validation accuracy value (val_acc) was obtained at the 82nd epoch with a value of 9.695, and the best validation loss value (val_loss) was 0.0044. The results showed that ticket demand increased while the results of the ticket demand model prediction in the following year decreased because it was predicted that many ticket requests from customers had been completed by the team.  This clearly shows how good the LSTM method is for the analysis of time series and sequential data.