Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique


  • Nathaniel Crosley Universitas Pradita, Tangerang, Indonesia
  • Ito Wasito Universitas Pradita, Tangerang, Indonesia




AI Integration, Efficiency, IT Support, Random Forest, Ticket Classification


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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How to Cite

Crosley, N. ., & Wasito, I. (2023). Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2283-2293.