<![CDATA[The network infrastructure of Serelo Lahat University, known for its prioritization of science and technology, is a crucial element in supporting various educational and administrative activities in the academic environment. However, the challenge of maintaining and managing this infrastructure demands a proactive approach. This research proposes a method that combines advanced data analysis and artificial intelligence to develop a proactive maintenance strategy. Through comprehensive data collection and analysis, a prognostic model is built to predict potential issues in the network. Integration of proactive maintenance strategies into the network management system enables rapid response to predicted issues. Evaluation and validation of the model demonstrate the effectiveness of this approach in improving the efficiency and reliability of the campus network infrastructure. Deployment and integration of this model in operational environments aim to enhance accurate maintenance decision-making. Recommendations for enhancing the effectiveness of proactive maintenance strategies are also proposed. Further research is hoped to evaluate the performance and reliability of the campus network infrastructure.]]>
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