Development of an Incident Prioritization Model Using Fuzzy Logic to Improve Quality and Productivity in IT Support Services

Hoorpah, Dristesh; Kishnah, Somveer; Pudaruth, Sameerchand

doi:10.1007/978-981-13-3338-5_7

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, to analyze the large data in this era, it is needed a new smart technology to perform an efficient decision-making support method. [5] [6] [7] Several studies discussed the use of machine learning to automate the incident management process, including research conducted by [5] which discusses about determining the priority of incidents using Fuzzy Mamdani. Research [8] [9] [10] also discusses the use of machine learning to determine incident categories.…”

Section: Introductionmentioning

confidence: 99%

Enhancing ITIL Incident Management: Innovative Machine Learning Approaches for Efficient Incident Prioritization and Resolution

Zahrothul Ain,

Safitri

2023

j. Teknik inform.

View full text Add to dashboard Cite

Incident Management in ITIL requires an effective process so the incidents do not disrupt business processes for too long. This research aims to automate decision-making in Incident Management process. To perform the automation in decision-making process requires machine learning algorithms. The development of machine learning method in this research will bring significance result such as a new technique of decision-making process in Incident Management, accelerate decision-making process in Incident Management by implementing machine learning to determine the category, group, and priority. By combining supervised and unsupervised machine learning, this research can help to determine the priority of the incident, so IT Operation Teams know which incident should resolve first. By training historical full description, short description, and title, machine learning can classify the new incident. In this research different classification algorithms are used to automate decision making process. Performances of automated decision-making are evaluated with accuracy, precision, recall, and f1-score. Based on the result of various performance metrics, classifier based on K-Nearest Neighbor performed well on predicting Priority, and both category and priority get the best performance with Support Vector Machine.

show abstract

Section: Introductionmentioning

confidence: 99%