Supervised and Unsupervised Learning in Data Mining for Employment Prediction of Fresh Graduate Students

Authors

  • Nor Azziaty Abdul Rahman Faculty of Art, Computing & Creative Industry, Sultan Idris Education University, 35900 Tanjong Malim, Perak Malaysia.
  • Kian Lam Tan Faculty of Art, Computing & Creative Industry, Sultan Idris Education University, 35900 Tanjong Malim, Perak Malaysia.
  • Chen Kim Lim Faculty of Art, Computing & Creative Industry, Sultan Idris Education University, 35900 Tanjong Malim, Perak Malaysia.

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,

Abstract

Data 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

References

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Published

2017-09-15

How to Cite

Abdul Rahman, N. A., Tan, K. L., & Lim, C. K. (2017). Supervised and Unsupervised Learning in Data Mining for Employment Prediction of Fresh Graduate Students. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-12), 155–161. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2787

Issue

Vol. 9 No. 2-12: The Challenge of Computer and Engineering in Building Sustainable Service Systems II

Section

Articles

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