Implementation of decision tree using C5.0 algorithm in preference and electability survey results on regional head election in Aceh

Abstract

Abstract The decision tree is one of the methods of classification in data mining. There are many algorithms used to construct the tree model; one of them is C5.0 algorithm. The tree model with C5.0 algorithm was carried out based on the survey result dataset of the preference and electability of the regional head selection pre-campaign year 2018 in one of the districts in Aceh. The datasets consisted of 5 predictor variables, i.e. sub-districts, age, main occupations, highest education, and attracting factors from regional head candidate candidates. Variable categories of decisions ranged from candidates A, B, C, and D. The distribution of datasets was divided into training data and testing data using the k-fold cross-validation method. The optimum tree model formation was based on the accuracy value of model and coefficient of Kappa. The result showed that the best tree model was constructed using testing data on S = 10. The accuracy of the model and the Kappa coefficient were 0.8427 and 0.7208, respectively. There were three rules generated with five nodes. The main predictor variable contributing to the optimum model was the attracting factor of candidates and sub-districts.