Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management

Authors

  • Resha Meiranadi Caturkusuma Information Systems, Universitas Dian Nuswantoro, Semarang, Indonesia
  • Farrikh Alzami Information Systems, Universitas Dian Nuswantoro, Semarang, Indonesia
  • Aris Nurhindarto Information Systems, Universitas Dian Nuswantoro, Semarang, Indonesia
  • MY Teguh Sulistiyono Information Systems, Universitas Dian Nuswantoro, Semarang, Indonesia
  • Candra Irawan Information Systems, Universitas Dian Nuswantoro, Semarang, Indonesia
  • Yupie Kusumawati Information Systems, Universitas Dian Nuswantoro, Semarang, Indonesia

DOI:

10.33395/sinkron.v9i1.14310

Keywords:

IT Service Management (ITSM), Incident Management, Duration Prediction, Random Forest Regressor, Machine Learning

Abstract

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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References

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How to Cite

Caturkusuma, R. M. ., Alzami, F., Nurhindarto, A. ., Sulistiyono, M. T. ., Irawan, C. ., & Kusumawati, Y. . (2025). Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 8-19. https://doi.org/10.33395/sinkron.v9i1.14310

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