Knowledge Management System (KMS) readiness level based on group areas of expertise to improve science education and computer science quality (cross-fertilization principle) (Case study: Computer science program course FPMIPA UPI)

Rizky Rachman Judhie Putra; Budi Laksono Putro

doi:10.1109/ICSITech.2017.8257203

Knowledge Management System (KMS) readiness level based on group areas of expertise to improve science education and computer science quality (cross-fertilization principle) (Case study: Computer science program course FPMIPA UPI)

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Rizky Rachman Judhie Putra, Budi Laksono Putro

2017 Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017 Vol. 2018-January Conference paper Cited by 3

Abstract

Computer Science Education courses concentrate on the scientific education of computer science, while the Computer Science Program concentrates on computer science. Both of these study programs have a close relationship to complement and improve each other in the domain of science and education in the field of computer science (cross-fertilization principle). Knowledge management nowadays is still in concept level that hard to implement and still requires exploration and improvement to develop. Failure level in implementation of Knowledge Management System (KMS) high enough that should be measured with KMS Readiness Levels. This research uses analytical hierarchy process (AHP) weighting method and Aydin/Tasci scales to find out the research objective which is priority weighting from each of concepts that has been studied and to find out readiness category based on Aydin/Tasci Scale. The initial phase is to design a scale of measurement using AHP method with normalization weight and priority values as final results the next phase is to analyze the processing values using Aydin/Tasci Scale to see readiness scale of knowledge management system in computer science program. As measured from the readiness level of KMS are knowledge manager, technology and culture. Based on that, AHP weight values for each concept which chosen for this KMS readiness research in Computer Science Program. The highest score is 3.176 from culture dimension. Average value KMS readiness in Computer Science Program is 2,863 which means Computer Science Program is not ready yet for KMS implementation. © 2017 IEEE.

Affiliations

Computer Science Department, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 229, Bandung, West Java, 40154, Indonesia

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