Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique
DOI:
10.33395/sinkron.v8i4.12925Keywords:
AI Integration, Efficiency, IT Support, Random Forest, Ticket ClassificationAbstract
This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative methods to assess effectiveness. of the Random Forest algorithm. This evaluation demonstrates the system's ability to correctly classify and prioritize incoming tickets. The successful implementation of this project at Company XYZ is a model for other organizations looking to optimize their IT support through AI-driven approaches. By providing simplified ticket classification and admission ticket reclassification based on AI algorithms, this research helps leverage AI technologies to improve IT support processes. Ultimately, the proposed solution benefits both support providers and users by improving efficiency, response times, and overall customer satisfaction.
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Rabah, S., Mokhov, S. A., Paquet, J. (2013). An Interactive Graph-based Automation Assistant. Proceedings of the 2013 Research in Adaptive and Convergent Systems. https://doi.org/10.1145/2513228.2513286
Herrera, V., Khoshgoftaar, T. M., Furht, B. (2019). Random Forest Implementation and Optimization For Big Data Analytics On Lexisnexis’s High Performance Computing Cluster Platform. J Big Data, 1(6). https://doi.org/10.1186/s40537-019-0232-1
Qamili, R., Shabani, S., Schneider, J. (2018). An Intelligent Framework for Issue Ticketing System Based on Machine Learning.. https://doi.org/10.1109/edocw.2018.00022
Ali, A.R. (2018) ‘Cognitive Computing To Optimize IT Services’, 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) [Preprint]. doi:10.1109/icci-cc.2018.8482078.
Amalia, S., Deborah, I. and Yulita, I.N. (2022) ‘Comparative Analysis of Classification Algorithm: Random forest, SPAARC, and MLP for airlines customer satisfaction’, SINERGI, 26(2), p. 213. doi:10.22441/sinergi.2022.2.010.
Anwar, M.T. (2021) ‘Automatic Complaints Categorization using Random Forest and Gradient Boosting’, Advance Sustainable Science, Engineering and Technology, 3(1), p. 0210106. doi:10.26877/asset.v3i1.8460.
Biau, G. and Scornet, E. (2016) ‘A Random Forest Guided Tour’, TEST, 25(2), pp. 197–227. doi:10.1007/s11749-016-0481-7.
Brüggen, S. and Holland, A. (2021) ‘Knowledge-based planning and controlling with methods of artificial intelligence to increase efficiency in IT projects’, 34th Bled eConference Digital Support from Crisis to Progressive Change: Conference Proceedings [Preprint]. doi:10.18690/978-961-286-485-9.52.
Dai, J. (2022) ‘Improving Random Forest Algorithm for University Academic Affairs Management System Platform Construction’, Advances in Multimedia, 2022, pp. 1–9. doi:10.1155/2022/8064844.
Engel, R. et al. (2022) ‘SLA-aware operational efficiency in AI-enabled service chains: Challenges ahead’, Information Systems and e-Business Management, 20(1), pp. 199–221. doi:10.1007/s10257-022-00551-w.
Katuwal, R. and Suganthan, P.N. (2018) ‘Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces’, 2018
Revina, A., Buza, K., Meister, V. (2020). IT Ticket Classification: The Simpler, The Better. IEEE Access, (8), 193380-193395. https://doi.org/10.1109/access.2020.3032840
International Joint Conference on Neural Networks (IJCNN) [Preprint]. doi:10.1109/ijcnn.2018.8489738.
Silva, S., Pereira, R. and Ribeiro, R. (2018) ‘Machine learning in incident categorization automation’, 2018 13th Iberian Conference on Information Systems and Technologies (CISTI) [Preprint]. doi:10.23919/cisti.2018.8399244.
Thomas, B. and J., C. (2020a) ‘Random Forest application on cognitive level classification of E-learning content’, International Journal of Electrical and Computer Engineering (IJECE), 10(4), p. 4372. doi:10.11591/ijece.v10i4.pp4372-4380.
S.P, P., C, Ramya., S, S. (2018). Classifying The Unstructured IT Service Desk Tickets using Ensemble of Classifiers.. https://doi.org/10.1109/csitss.2018.8768734
Elliott, D., & Soifer, E. (2022). AI Technologies, Privacy, and Security. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.826737
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