MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT

MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT

prediction, regional election, political party, machine learning, data mining

In this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of
candidates in Regional Elections in Indonesia based on data taken from General Election Commission
(KPU). The data consist of composition of political parties that support each candidate. The purpose of
this research is to develop a Machine Learning model based on verified data provided by official
institution to predict the victory of each candidate in a Regional Election instead of using social media
data as in previous studies. The prediction itself simply a classification task between two classes, i.e.
‘win’ and ‘lose’. Several Machine Learning Algorithms were applied to find the best model, i.e. kNearest Neighbors, Naïve Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest
performance. We found in this research that Neural Networks with Multilayer Perceptron is the best
model with 74.20% of accuracy.

Muhammad Fachrie

http://dx:doi:org/10:21609/jiki:v13i2.860

Faculty of Computer Science Universitas Indonesia

2020-06-30

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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English

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)