A DECISION TREE CLASSIFIER FOR PREDICTING VOTER TURNOUT IN MALAYSIAN GENERAL ELECTION
Keywords:
Voters’ Turnout, Data Mining, Decision Tree Classification, CHAID, CART, C5.0Abstract
Malaysia practices democracy in shaping the country’s future. Hence, each citizen is entitled to have a vote in her election. Since independence, Malaysia had undergone 14 general elections. The 84.8% voters’ turnout rate in 2013 general election is the highest turnout rate that has been recorded in the Malaysian election history. However, when voters’ turnout rate is being compared with voting age population and the number of eligible voters, the actual participation rate is considered low. Thus, the main objective of this study is to predict Malaysian voters’ turnout in the 2008 and 2013 general elections using classification tree algorithms. The datasets used in this study are the Asian Barometer Survey datasets. Datasets of 2014 and 2010 were used to examine the factors that determine voters’ turnout in 2013 and 2008, respectively. Three selection decision tree algorithms used in this study are CHAID, CART, and C5.0. It is found that between these three methods, CHAID perform the best in predicting Malaysian voters’ turnout during the general election. However, other feasible approaches such as Support Vector Machine (SVM), Random Forest and Boosting C5.0 can also be used and evaluated to predict voters’ turnout.
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