Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Abstract

e-ticketing is one of the common applications used in technical support in Information Technology (IT) and has been used worldwide in any field of company. The benefits of e-ticketing can reduce the human efforts, increase the sufficiency of system and provides the benefits and efficiency to the customers. Also, e-ticketing ticketing system can enhance worker safety, improving productivity, increasing project efficiency and cause the good impact on the performance of the business in terms of profitability. The main objective of this study is defining the model performance in each important feature by analysing the complex dataset by using logistic regression as a Machine Learning (ML) algorithm. In evaluation the performance of classifier, the dataset is injected to Python programming and split into 90% as training set and 10% for the testing set. From the analysis, the study found that only 3 out of 11 independent features in dataset that are relevant chosen to proceed the ML analysis. From the result, the accuracy for sct_short_description, sct_cmdb_ci, and sct_assignment_group is 41.65%, 48.77% and 96.49%, respectively. It showed that the accuracy’s result for the sct_assignment_group resulted that the model is very good accuracy and indicate that the model is well performing. Meanwhile, the value of F1-score is 96.11% in each feature. This result indicates that the model has a good balance of precision and recall in its binary classification predictions. Hence, the study considers the sct_assignment_group as a best features to proceed the analysis. The future study will consider dealing the combination of complexity features by implementing more analysis on ML such as Support Vector Machines and Naïve Bayes.