Performance Evaluation of Machine Learning Approaches to Optimize IT Service Management

  • Mohammad Agus Prihandono, Riri Fitri Sari

Abstract

IT service management experienced a variety of incidents that can be categorized as
incident attributes. Incident attributes can be considered as an important IT cycle
relationship in an organization. These attributes of the incident might improve the event
response of an IT cycle in the occurrence of disruption and to identify the lowest possible
impact in an organization. In this research, we used support vector machine (SVM)
classifier, using both genetic algorithm (GA) and particle swarm optimization (PSO)
optimization technique. The aim is to gain better accuracy. Furthermore, rules are
extracted to recognize the weight of IT incident. Our experiment result shows that SVM
with optimization algorithms using particle swarm optimization is better than using
genetic algorithm.

Published
2020-05-01
How to Cite
Mohammad Agus Prihandono, Riri Fitri Sari. (2020). Performance Evaluation of Machine Learning Approaches to Optimize IT Service Management. International Journal of Advanced Science and Technology, 29(7s), 3518-3525. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/17646