UPSI Digital Repository (UDRep)
|Abstract : Universiti Pendidikan Sultan Idris|
| In 2017, the global unemployment rate is projected around 5.6% while for 2018 the unemployment rate is 5.5% which is little bit decrease. However, the youth (aged 15 to 24) unemployment rate in Malaysia is over three times higher at around 10.8% in 2017. In addition, Malaysia achieved the second highest rate after Indonesia (15.6%) compare to other countries in Asian including China (10.8%), India (10.5%), Singapore (4.6%), Vietnam (7%), Thailand (5.9%) and Philippines (7.9%). This study aim to present a set of data mining algorithms to find the most important factor of employability among the fresh graduate students. The comparison for six data mining algorithms which are 1) Logistic Regression, 2) Decision Tree, 3) Naive Bayes, 4) KNearest Neighbor, 5) Support Vector Machine and 6) Neural Network by using split validation method which is 70-30 as a ratio. Based on the result, Neural Network is the best classifier other than another five algorithms. The Neural Network Model showed 6 majors effect on employability are 1) willing to face challenges of the outside world and work, 2) can communicate effectively, 3) field of technical, 4) convocation on October and 6) Sex (Male). The predictive model of employability will benefit the management of the higher education, Ministry of Education and fresh graduate itself to predict the employability status either employed and unemployed by graduate data.|
1. Deepak, E., Pooja, G. S., Jyothi R. N. S., Venkatrama, P. K. S.: SVM Kernel based Predictive Analystics on Faculty Performance Evalation, 1-4 (2016)
2. Shafie, L.A, Nayan, S.: Employability Awareness among Malaysian Undergraduates. International Journal of Business and Management, 5(8):119—123 (2010)
3. Xu, W., Li, Z., Cheng, C., & Zheng, T. (2012). Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 33–42. https://doi.org/10.1007/s11761-012-0122-2
4. Mishra, T. (2016). Students ’ Employability Prediction Model through Data Mining, 11(4), 2275–2282.
5. Tajul, M., Ab, R., & Yusof, Y. (2016). Graduates Employment Classification using Data Mining Approach, 20002. https://doi.org/10.1063/1.4960842
6. Gao, L. (2015). Analysis of Employment Data Mining for University Student based on Weka Platform, 2(4), 130–133.
7. Jantawan, B., & Tsai, C. (2013). The Application of Data Mining to Build Classification Model for Predicting Graduate Employment. International Journal of Computer Science and Information Security, 11(10), 1–8. https://doi.org/10.1016/j.bdr.2015.01.001
8. Affendey, L. S., Paris, I. H. M., Mustapha, N., Sulaiman, M. N., and Muda, Z, “Ranking of influencing factors in predicting student academic performance”, Information Technology Journal, Vol. 9, No. 4, pp. 832-837, 2010.
9. Kumar, V. and Chadha, A., “An Empirical Study of the Applications of Data Mining Techniques in Higher Education”, International Journal of Advanced Computer Science and Applications, Vol. 2, pp. 80-84, 2011.
10. Arsad, P. M., Buniyamin, N., & Manan, J. A. (2014). Neural Network and Linear Regression Methods for Prediction of Students ’ Academic Achievement, (April), 916–921.
11. Huang, J. (2014). Hardiness , Perceived Employability , and Career Decision Self-Efficacy Among Taiwanese College Students, (415), 1–14. https://doi.org/10.1177/0894845314562960
|This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.|
You may use the digitized material for private study, scholarship, or research.