Automatic Issue Classification Feature and Generative Standard Operating Procedure in IT Helpdesk Application PKG V2 at PT. Petrokimia Gresik - Hardian, Shera Zahra Alya Nasywa; Sukaridhoto, Sritrusta; Prasetyo, Joko (TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER, 2025)
Effective Information Technology (IT) incident management is a crucial need in large-scale enterprise environments such as PT Petrokimia Gresik. Therefore, this final project develops an automated issue prioritization classification feature and an AI-based generative solution mechanism within the Helpdesk TI PKG V2 application. By utilizing the Naïve Bayes algorithm for issue classification and integrating the ChatGPT API for automated SOP-based solution generation, the system aims to overcome the limitations of previous manual processes. The expected outcomes include improved efficiency in incident handling, higher accuracy in prioritization, and faster responses through relevant and informative solutions. During testing, the incident classification model demonstrated satisfactory performance, particularly in identifying extreme urgency levels. Additionally, the automatically generated SOP solutions proved to be relevant and aligned with internal handling procedures. The system is evaluated through functional testing and user acceptance testing to ensure its optimal implementation in a dynamic and complex work environment.