基于案例库重排和K-近邻方法的信息技术支持障碍识别智能系统

Muhamad Dedi Suryadi, S. Sahlan, Ndaru Ruseno
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引用次数: 0

摘要

延迟处理和解决硬件/软件方面的IT问题会极大地影响公司的业务流程。有时,IT服务台人员仍然很难找到解决方案并做出决策来解决IT问题。这是因为目前还没有一个系统可以帮助IT服务台人员找到能够快速准确地解决所面临问题的解决方案。在寻找解决方案时,所面临的问题无法显示合适或接近现有历史数据的解决方案。在识别IT问题时,作者使用基于案例的推理或基于案例的推断(CBR),通过使用K-最近邻(K-NN)算法来执行查找具有最高接近度和接近度测量的案例的过程。本研究旨在解释基于案例的反应(CBR)和K-最近邻(K-NN)算法在识别IT问题扰动中的应用。结果表明,经过5位专家的测试,基于事例的重新分配和K-最近邻算法的应用得到了准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Penerapan Intelligence System berbasis Case Base Reasioning dan Metode K-Nearest Neighbor Untuk Identifikasi Gangguan IT Support
Delay in handling and solving IT problems both hardware / software greatly affects the company's business processes. Sometimes, IT helpdesk personnel still difficult to find solutions and make decisions to solve IT problems. This is because there is currently no system that can help IT Helpdesk personnel in finding solutions to be able to solve the problems being faced quickly and accurately. When going to look for solutions to the problems being faced can not display solutions that are appropriate or close to the existing historical data. In identifying IT problems, the authors use case-based reasoning or Case-Based Reasoning (CBR) by carrying out the process of finding the case with the highest proximity and proximity measurement using the K-Nearest Neighbor (k-NN) algorithm. This study aims to explain the application of Case-Based Reactioning (CBR) and K-Nearest Neighbor (k-NN) Algorithm to identify IT problem disturbances. The results showed that the application of Case-Based Reasioning and K-Nearest Neighbor (k-NN) Algorithm after being tested by 5 Experts got accurate results.
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