Graduates employment classification using data mining approach - Ab Aziz, Mohd Tajul Rizal; Yusof, Yuhanis (IP Publishing LLC, 2016)
Data Mining is a platform to extract hidden knowledge in a collection of data.This study investigates the suitable classification model to classify graduates employment for one of the MARA Professional College (KPM) in Malaysia.The aim is to classify the graduates into either as employed, unemployed or further study.Five data mining algorithms offered in WEKA were used; Naïve Bayes, Logistic regression, Multilayer perceptron, k-nearest neighbor and Decision tree J48.Based on the obtained result, it is learned that the Logistic regression produces the highest classification accuracy which is at 92.5%. Such result was obtained while using 80% data for training and 20% for testing.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.