Alex Wenda, - Knowledge Data Discovery (Frequent Pattern Growth): The Association Rules for Evergreen Activities on Computer Monitoring. In: International Conference on Intelligent and Fuzzy Systems 2020.
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Abstract
The aim of this research paper is to construct a set of guidelines that can improve the quality and efficiency of knowledge data discovery process by utilizing various types of knowledge domains. In addition, this paper offered the way of how the knowledge domain could be adopted for helping the system developer. The methodologies contain various scenarios of data exploring and the authors used data mining approach. The paper shows evidence of important to adopt data mining methods in the industry sector as well as the advantages and disadvantages. Evergreen human machine interface (HMI) at PT. Chevron Pacific Indonesia (CPI) is kind of activities to maintenance computer equipment. Nowadays, the errors were frequently happened in the accuracy of computer maintenance which has a profound effect on production results. Therefore, this study focuses on the rules of association using the frequent pattern growth algorithm (FP-growth) which is producing knowledge with trust value of 100% and a support value is 95%. The value results of support and confidence are the new approach and knowledge for the management level to decide decisions in the evergreen activities process.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | 000 Karya Umum |
| Depositing User: | fsains - |
| Date Deposited: | 21 Feb 2023 08:00 |
| Last Modified: | 21 Feb 2023 08:00 |
| URI: | http://repository.uin-suska.ac.id/id/eprint/69007 |
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