Supervised and Unsupervised Learning in Data Mining for Employment Prediction of Fresh Graduate Students
Keywords:Data Mining, CRISP-DM, Rapid Miner Supervised and Unsupervised Machine Learning Algorithm, K-Nearest Neighbor, Naïve Bayes, Decision Tree, Neural Network, Logistic Regression and Support Vector Machine,
AbstractData mining techniques are widely used in engineering, medicine, industry, agriculture and even used in education to predict a future situation. In this paper, the used of data mining techniques applied in features selection and determine the best model that can be used to predict the employment status of fresh graduate Public Institutions either employed or unemployed, six months after graduation. In CRISP-DM methodology, six phases were adopted. The algorithm in supervised and unsupervised learning; K-Nearest Neighbor, Naive Bayes, Decision Tree, Neural Network, Logistic Regression and Support Vector Machines were compared using the training data set from Tracer Study to determine the highest accuracy in turn is used as a predictive model. Rapid Miner as a data mining tool was used for data analysis algorithm
Aziz, A. A., Ismail, N. H., Ahmad, F., & Hassan, H. (2015). A framework for students??? academic performance analysis using na??ve bayes classifier. Jurnal Teknologi, 75(3), 13–19. https://doi.org/10.11113/jt.v75.5037.
Gao, L. (2015). Analysis of Employment Data Mining for University Student based on Weka Platform, 2(4), 130–133 A Comparison. International Journal of Innovative Research in Computer and Communication Engineering (An ISO Certified Organization), 3297(6), 4584–4588. Retrieved from www.ijircce.com
Jantawan, B., & Tsai, C. (2007). A Classification Model on Graduate Employability Using Bayesian Approaches:
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
Kementerian Pengajian Tinggi Malaysia. (2015). Status Pekerjaan Graduan (Warganegara) 2015 (p. 46).
Masethe, M. A., & Masethe, H. D. (2014). Prediction of Work Integrated Learning Placement Using Data Mining Algorithms, I, 22–24.
Mishra, T. (2016). Students ’ Employability Prediction Model through Data Mining, 11(4), 2275–2282.
Sapaat, M. A., Mustapha, A., Ahmad, J., & Chamili, K. (2011). A Data Mining Approach to Construct Graduates Employability Model in Malaysia, 1(4), 1086–1098.
Tajul, M., Ab, R., & Yusof, Y. (2016). Graduates Employment Classification using Data Mining Approach, 20002. https://doi.org/10.1063/1.4960842
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
Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Michelle, L. (2016). Fresh Graduate Unemployment in Malaysia | EduAdvisor. Retrieved from https://eduadvisor.my/articles/what-didnt-know-fresh-graduate-unemployment-malaysia-infographic/
Employers Pick Personality over Qualification _ JobStreet. (n.d.). Retrieved from http://www.jobstreet.com.my/career-esources/employers-pick-personality-qualification/#.WKMqX9J97IU
International, T., & Of, J. (2016). Proposed Model for Predictive Mapping of Graduate ’ s Skills to Industry Roles Using Machine Learnining Techniques Fullgence Mwachoo Mwakondo , 2 Dr . Lawrence Muchemi , 3 Prof . Elijah Isanda, 15–24.
Ramamohan, Y., Vasantharao, K., Chakravarti, C. K., & Ratnam, a S. K. (2012). A Study of Data Mining Tools in Knowledge Discovery Process. International Journal of Soft Computing and Engineering, 2(3), 191–194. Retrieved from http://www.ijraset.com/fileserve.php?FID=3979
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