Page 94 - ICGE VII - ABSTRACT BOOK

INTERNATIONAL CONFERENCE ON GLOBAL EDUCATION VIII
“Visioning the Future of Education”

PREDICTIVE ANALYSIS USING STUDENTS’ ENROLLMENT DATA
IN MALAYSIA POLYTECHNICS

Siti Zuhra Abu Bakar

(ctzuhra@gmail.com)

Abstract

This research aims to present the prediction analysis of student’s choice of program
and enrollment in higher education institutions especially for polytechnic using data
mining and classifications. It is important for higher institutions to provide quality
education to their students. Placing and allocating students based on their choices
can be challenging task as it requires many criteria to be considered. Malaysia
Polytechnic also did not utilize data mining and data science in predicting their
student’s enrollment. The main goal of this research is to predict student’s offered
program based on their choices and prediction of students’ enrollment in
polytechnic by each state in Malaysia. This research also gives feasible solution that
guide administrators to provide better education quality by analyzing the students’
enrollment dataset by exploring Cross Industry Standard Process for Data Mining
(CRISP-DM) using descriptive data mining approach and proposed predictive
model for offered course based on students’ choices. The predictive model
implementing classification approach using Naive Bayes, Decision Tree: J48 and
Artificial Neural Network (MLP with Back Propagation). The results show that
Decision Tree J48 has the highest accuracy than Naive Bayes and Artificial Neural
Network (Multilayer Perceptron (MLP) with Back Propagation) algorithms.

Keywords: Predictive Analysis, enrollment, data science, classification.

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