TY  - JOUR
T1  - Application of Data Mining in Forecasting Graduates Employment
AU - Tajul Rizal, Mohd AU - Yusof, Yuhanis 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 16
SP  - 4202
EP  - 4207
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.4202.4207
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.4202.4207
KW  - Data mining
KW  -graduates employment
KW  -Naive Bayes
KW  -logistic regression
KW  -multilayer perceptron
KW  -K-nearest neighbor
KW  -decision tree
AB  - Obtaining information on graduate employability is crucial to every higher education institution. This
is because such data would provide insight on the effectiveness of the institution curriculum in preparing
human capital for the market needs. To date, the MARA Professional College (KPM) in Malaysia relies on
graduates to manually provide data on their employment. Such an approach is not reliable as not all graduates
provide the information to the institution. This study presents the application of data mining techniques in
forecasting the KPM graduates employment type. In data mining, there exist three main tasks; classification,
clustering, and association mining. The aim of this study is to forecast whether a particular graduate will be
"employed", "unemployed" or "further study" 6 months after the completion of his study. The undertaken
experiments include the utilization of five data mining techniques, namely, the Naive Bayes, Logistic regression,
multilayer perceptron, K-nearest neighbor and decision tree. Furthermore, the experimental setup-up is based
on three types of data proportion (training-testing) 70-30, 80-20 and 90-10. Based on the obtained result, it is
learned that the Logistic regression is the best classifier for the in-hand dataset. In particular, the classifier is
at its best when the 80-20 proportion is adopted. The produced classification model will benefit the management
of the college as it provides insight to the quality of graduates that they produce and how their curriculum can
be improved to cater the needs from the industry.
ER  -