Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management
DOI:
10.33395/sinkron.v9i1.14310Keywords:
IT Service Management (ITSM), Incident Management, Duration Prediction, Random Forest Regressor, Machine LearningAbstract
In the digital era, ensuring customer satisfaction with IT services is crucial for business success. However, the complexity of IT infrastructure makes it difficult to manage services, requiring companies to focus on improving efficiency and reducing operational costs. One of the strategies used is Information Technology Service Management (ITSM), the main component of which is incident management, which aims to minimize service disruptions. While various studies on ITSM exist, research focused on Machine Learning models for predicting incident resolution times is relatively limited. This research aims to develop an incident resolution duration prediction model using a Random Forest Regressor-based regression approach. The dataset used is an event log from the ServiceNow system containing data on 24,918 incidents. The model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 metrics, where the model achieved a MAE of 14.33 hours, RMSE of 69.8 hours, and R2 of 0.98. These results show that the model can provide accurate predictions and support better decision-making in IT incident handling. Time-related features, such as sys_update_month and closed_month, proved to be the most influential factors in predicting incident resolution duration.
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Adewale, M. D., Ebem, D. U., Awodele, O., Sambo-Magaji, A., Aggrey, E. M., Okechalu, E. A., Donatus, R. E., Olayanju, K. A., Owolabi, A. F., Oju, J. U., Ubadike, O. C., Otu, G. A., Muhammed, U. I., Danjuma, O. R., & Oluyide, O. P. (2024). Predicting gross domestic product using the ensemble machine learning method. Systems and Soft Computing, 6, 200132. https://doi.org/10.1016/j.sasc.2024.200132
Agarwal, R., & Dhingra, S. (2023). Factors influencing cloud service quality and their relationship with customer satisfaction and loyalty. Heliyon, 9(4), e15177. https://doi.org/10.1016/j.heliyon.2023.e15177
Ahmed, S., Singh, M., Doherty, B., Ramlan, E., Harkin, K., Bucholc, M., & Coyle, D. (2023). An Empirical Analysis of State-of-Art Classification Models in an IT Incident Severity Prediction Framework. Applied Sciences, 13(6), 3843. https://doi.org/10.3390/app13063843
Ali, R. F., Dominic, P. D. D., Ali, S. E. A., Rehman, M., & Sohail, A. (2021). Information Security Behavior and Information Security Policy Compliance: A Systematic Literature Review for Identifying the Transformation Process from Noncompliance to Compliance. Applied Sciences, 11(8), 3383. https://doi.org/10.3390/app11083383
Baptista, B., & Barata, J. (2024). Continuously Improving IT Service Management in the Pharmaceutical Industry. Procedia Computer Science, 239, 923–930. https://doi.org/10.1016/j.procs.2024.06.253
Claudio Amaral, M. F. (2018). Incident management process enriched event log [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C57S4H
Das, R., Kasieczka, G., & Shih, D. (2022). Feature Selection with Distance Correlation (No. arXiv:2212.00046). arXiv. http://arxiv.org/abs/2212.00046
Erian, K. H., Regalado, P. H., & Conrad, J. M. (2021). Missing data handling for machine learning models. IAES International Journal of Robotics and Automation (IJRA), 10(2), 123. https://doi.org/10.11591/ijra.v10i2.pp123-132
Gunawan, H., Irianto, A. B. P., & Negara, J. G. P. (2024). IMPLEMENTATION OF SUSTAINABLE SERVICE IMPROVEMENT IN ORGANIZATIONS USING FRAMEWORK INFORMATION TECHNOLOGY INFRASTRUCTURE LIBRARY (ITIL). Procedia Computer Science, 234, 748–755. https://doi.org/10.1016/j.procs.2024.03.061
Kebede, Y. B., Yang, M.-D., & Huang, C.-W. (2024). Real-time pavement temperature prediction through ensemble machine learning. Engineering Applications of Artificial Intelligence, 135, 108870. https://doi.org/10.1016/j.engappai.2024.108870
Khalid, B. (2024). Evaluating customer perspectives on omnichannel shopping satisfaction in the fashion retail sector. Heliyon, 10(16), e36027. https://doi.org/10.1016/j.heliyon.2024.e36027
Kuleshov, V. V., Aygumov, T. G., Zolkin, A. L., Tychkov, A. S., & Bityutskiy, A. S. (2023). Use of machine learning for prevention of incidents and reduction of occupational risks at the workplace. In A. Gibadullin & G. Khalmatjanova (Eds.), International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023) (p. 6). SPIE. https://doi.org/10.1117/12.2680682
Kurian, D., Sattari, F., Lefsrud, L., & Ma, Y. (2020). Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations. Safety Science, 130, 104873. https://doi.org/10.1016/j.ssci.2020.104873
Luftensteiner, S., Chasparis, G. C., & Küng, J. (2024). PAS - A Feature Selection Process Definition for Industrial Settings. Procedia Computer Science, 232, 308–316. https://doi.org/10.1016/j.procs.2024.01.030
Nikulin, V., Shibaikin, S. D., & Vishnyakov, A. N. (2021). Application of machine learning methods for automated classification and routing in ITIL. Journal of Physics: Conference Series, 2091(1), 012041. https://doi.org/10.1088/1742-6596/2091/1/012041
Ntobongwana, L., & Telukdarie, A. (2024). A critical review of global best practices elements in digital technologies: Advancing a theoretical architecture for quality engineering. Journal of Industrial Information Integration, 41, 100665. https://doi.org/10.1016/j.jii.2024.100665
Palma, A., Acitelli, G., Marrella, A., Bonomi, S., & Angelini, M. (2024). A compliance assessment system for Incident Management process. Computers & Security, 146, 104070. https://doi.org/10.1016/j.cose.2024.104070
Polaganga, R. K., & Liang, Q. (2024). Ensemble prediction of RRC session duration in real-world NR/LTE networks. Machine Learning with Applications, 17, 100564. https://doi.org/10.1016/j.mlwa.2024.100564
Sadeghi, B., Molayemat, H., & Pawlowsky-Glahn, V. (2024). How to choose a proper representation of compositional data for mineral exploration? Journal of Geochemical Exploration, 259, 107425. https://doi.org/10.1016/j.gexplo.2024.107425
Salamah, A. A., Hassan, S., Aljaafreh, A., Zabadi, W. A., AlQudah, M. A., Hayat, N., Al Mamun, A., & Kanesan, T. (2022). Customer retention through service quality and satisfaction: Using hybrid SEM-neural network analysis approach. Heliyon, 8(9), e10570. https://doi.org/10.1016/j.heliyon.2022.e10570
Santos, S. B. M. G., & Rodrigues, N. J. P. (2024). ServiceNow: Implications and Practice within the Business Environment. Procedia Computer Science, 239, 11–18. https://doi.org/10.1016/j.procs.2024.06.140
Sarwar, M. I., Abbas, Q., Alyas, T., Alzahrani, A., Alghamdi, T., & Alsaawy, Y. (2023). Digital Transformation of Public Sector Governance With IT Service Management–A Pilot Study. IEEE Access, 11, 6490–6512. https://doi.org/10.1109/ACCESS.2023.3237550
Shah, D., Xue, Z. Y., & Aamodt, T. M. (2022). Label Encoding for Regression Networks (No. arXiv:2212.01927). arXiv. http://arxiv.org/abs/2212.01927
Terpoorten, C., Klein, J. F., & Merfeld, K. (2024). Understanding B2B customer journeys for complex digital services: The case of cloud computing. Industrial Marketing Management, 119, 178–192. https://doi.org/10.1016/j.indmarman.2024.04.011
Ulu, M., Türkan, Y. S., Mengüç, K., Namlı, E., & Küçükdeniz, T. (2024). Dynamic Forecasting of Traffic Event Duration in Istanbul: A Classification Approach with Real-Time Data Integration. Computers, Materials & Continua, 80(2), 2259–2281. https://doi.org/10.32604/cmc.2024.052323
Verdonck, T., Baesens, B., Óskarsdóttir, M., & Vanden Broucke, S. (2024). Special issue on feature engineering editorial. Machine Learning, 113(7), 3917–3928. https://doi.org/10.1007/s10994-021-06042-2
Widianto, A., & Subriadi, A. P. (2022). IT service management evaluation method based on content, context, and process approach: A literature review. Procedia Computer Science, 197, 410–419. https://doi.org/10.1016/j.procs.2021.12.157
Zahrothul Ain, A. A., & Safitri, C. (2023). Enhancing ITIL Incident Management: Innovative Machine Learning Approaches for Efficient Incident Prioritization and Resolution. JURNAL TEKNIK INFORMATIKA, 16(2), 204–214. https://doi.org/10.15408/jti.v16i2.31439
Zhang, J. (2024). Impact of an improved random forest-based financial management model on the effectiveness of corporate sustainability decisions. Systems and Soft Computing, 6, 200102. https://doi.org/10.1016/j.sasc.2024.200102
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Copyright (c) 2025 Farrikh Alzami, Resha Meiranadi Caturkusuma, Aris Nurhindarto, MY Teguh Sulistiyono, Candra Irawan, Yupie Kusumawati

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