The TODIM is a decision-making method that can examine the psychological behavior of decision-makers (DMs). However, the traditional TODIM method has still not been having the ability to overcome fuzzy information such as interval values and linguistic variables. This paper proposes an extended TODIM decision-making model for multiple-attribute decision-making (MADM) problems in a linguistic environment using dual-connection numbers (DCNs). The extended model uses linguistic variables in which the values of alternatives and criteria for both of them are formatted in the triangular fuzzy numbers (TFNs) to express the uncertain information. First, some definitions and basic operators of the TFNs and DCNs are introduced. Then, the way how to convert fuzzy information in forms of the TFNs into DCNs and the step how to transform each criterion weight value into a crisp value using the defuzzification of Minkowski are demonstrated. Furthermore, the traditional TODIM is improved to address MADM problems with DCNs, and detailed calculation steps in determining decisions are explained. Finally, an illustrative example which is a cadre selection problem is applied to demonstrate the conformity and validity of the extended TODIM method and to compare it with some other methods.

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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.

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