Knuth Morris Pratt - Boyer Moore Hybrid Algorithm for Knowledge Management System Model on Competence Employee in Petrochemical Company

Knowledge Management System (KMS) is a system that allows a company to create, obtain, store and use knowledge optimally which is useful for improving company progress. Knowledge storage is useful for maintaining knowledge, therefore a string search method is needed to access the existing knowledge in KMS, so that if the knowledge is needed then other employees can easily get it. In this research a knowledge search method will be conducted in the form of employee competencies in KMS, to get it back a string search must be performed to make it easily accessible to other employees. The search method in this study is a combination of the search Algorithm Knuth Morris Pratt and Boyer Moore. In this study, the combined Knuth Morris Pratt-Boyer Moore (KMP-BM) Hybrid Algorithm is used for modeling the knowledge management system in determining employee competencies in petrochemical industry companies. Employee competency data are the result of competency assessment of the company which is then grouped by human resource development in KMS. The results of this study are looking for a method of string matching with the application of the KMP-BM Hybrid Algorithm on the KMS model to store various knowledge about employee competencies in the company. By using a combination of the existing advantages KMP and BM Algorithm, searching for KMS can be done more quickly and accurately.