A Knowledge Management System for Assessing Lecturer Competence in Indonesian Higher Educational Institutions
Government Regulation No. 37 of 2009 concerning Lecturers emphasizes that lecturer competency development and coaching must be conducted periodically and continuously. However, in the current era of global development, as the world of education enters the Industrial Revolution 4.0, the primary chall...
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| Format: | Thesis |
| Language: | en en |
| Published: |
2025
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| Subjects: | |
| Online Access: | http://ur.aeu.edu.my/1460/1/Thesis%20Undang%20Syaripudin-1-24.pdf http://ur.aeu.edu.my/1460/2/Thesis%20Undang%20Syaripudin.pdf http://ur.aeu.edu.my/1460/ https://online.fliphtml5.com/sppgg/wqgr/ |
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| Summary: | Government Regulation No. 37 of 2009 concerning Lecturers emphasizes that lecturer competency development and coaching must be conducted periodically and continuously. However, in the current era of global development, as the world of education enters the Industrial Revolution 4.0, the primary challenge facing higher education institutions is how to adapt lecturer competency development to current trends. Higher education institutions still face limitations in their knowledge management systems, resulting in slow knowledge transfer and collaboration between lecturers. The goal of developing lecturer competencies is to support their career advancement. The role of the Knowledge Management System (KMS) in developing lecturer competencies is very important because it is practically easy to access and allows independent competency development. There are several stages in developing a KMS, namely compiling data requirements for the four lecturer competencies: pedagogical competency, professional competency, personality competency, and social competency. Next, crawling of all applications related to the Tridharma of higher education, namely education, research, and community service, is carried out using web services methods. Competency development is carried out on this KMS by downloading lecturer competency materials as needed and conducting discussion forums among colleagues. Lecturer competency measurement is carried out by first checking employee status using the SVM algorithm with an accuracy value of 72.28%, then using a hybrid SVM and PSO algorithm with an accuracy value of 100%. The comparison results show an increase in accuracy of 27.91% using the hybrid SVM and PSO algorithm. The next stage involves measuring lecturer competency by having 20 lecturers input answers to 24 essay questions, each with a 150-word limit. The results of the LSA algorithm combined with the OpenAI algorithm are compared with the results of expert assessments. Based on this comparison, the RMS value is 8.2%. This means that the accuracy of the developed system in measuring lecturer competency is 91.8%. |
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