Prediction of public participation in elections is one measure of election success. Voter participation is at the polling station level and involves four data sources: voters, polling stations, recapitulation, and village profiles. The preprocessing stage is carried out on each dataset, including maintenance, transformation, and integration. Two types of datasets are defined, involving all attributes and removing the result from attribute correlation. Classification method with five machine learning algorithms (ML) with participation prediction classes labelled High and Low. The highest result is 85.90% for the type 1 dataset and Artificial Neural Network (ANN) algorithm with 60% training and 40% testing split dataset. Furthermore, for detachment type 2, by eliminating several attributes, 100% results are obtained for the K-Nearest Neighbor (kNN) algorithm with a split dataset of 70% training and 30% testing. Of the five ML algorithms, only the Naïve Bayes (NB) algorithm did not experience an increase in prediction results. Furthermore, the significant influence of attributes on the prediction class is shown in each attribute of the dataset, including the Permanent Voter List at the Polling Place (DPT TPS), Local, Health Access and Total Recapitulation.

Original languageEnglish
Title of host publicationICon EEI 2022 - 3rd International Conference on Electrical Engineering and Informatics
Subtitle of host publicationSustainable Engineering for Industrial Revolution 4.0, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665454346
Publication statusPublished - 2022
Event3rd International Conference on Electrical Engineering and Informatics, ICon EEI 2022 - Virtual, Online, Indonesia
Duration: 19 Oct 202220 Oct 2022

Publication series

NameProceedings of the International Conference on Electrical Engineering and Informatics
ISSN (Print)2155-6830


Conference3rd International Conference on Electrical Engineering and Informatics, ICon EEI 2022
CityVirtual, Online


  • attribute selection
  • classification
  • community participation rate
  • election voting


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