Automation of Server Management Tasks and Data Aggregation using Generative AI

Machine learning has revolutionized industry practices by addressing complex operational challenges. Overheating server temperature due to administrative negligence at Institut Teknologi Sepuluh Nopember’s Directorate of Technology and Information Systems Development (DPTSI) is a problem that can be solved with the help of machine learning. This research aims to create a user-friendly tool that enables DPTSI’s server administrators to predict and prevent server-related problems effectively, thereby reducing downtime. The results of this study were obtained by the combination of iTransformer as a prediction model, Random Forest as a classification model, and Mistral-7B as a generative AI model deployed on a web-based application used to help server administrator decision-making abilities by predicting future server conditions and providing solutions on how to resolve predicted problems. This research contributes to the advancement of machine learning applications in industrial society, offering a practical solution for server management through innovative model combinations and implementation strategies.