Abstract
This study is to construct the Graduates Employability Model using data mining approach, in specific the classification task. To achieve it, we use data sourced from the Tracer Study, a web-based survey system from the Ministry of Higher Education, Malaysia (MOHE) since 2009. The classification experiment is performed using various Bayes algorithms to determine whether a graduate has been employed, remains unemployed or in an undetermined situation. The performance of Bayes algorithms are also compared against a number of tree-based algorithms. In conjunction with tree-based algorithm, Information Gain is used to rank the attributes and the results showed that top three attributes that have direct impact on employability are the job sector, job status and reason for not working. Results showed that J48, a variant of decision-tree algorithm performed with highest accuracy, which is 92.3% as compared to the average of 90.8% from other Bayes algorithms. This leads to the conclusion that a tree-based classifier is more suitable for the tracer data due to the information gain strategy.
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Sapaat, M.A., Mustapha, A., Ahmad, J., Chamili, K., Muhamad, R. (2011). A Classification-Based Graduates Employability Model for Tracer Study by MOHE. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_25
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DOI: https://doi.org/10.1007/978-3-642-22389-1_25
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