Indonesian Journal of Cultural and Community Development

Vol. 16 No. 2 (2025): June
Community Education Development Articles

Decision Tree Analysis for Predicting Voter Participation Using IDM Data: Analisis Pohon Keputusan untuk Memprediksi Partisipasi Pemilih Menggunakan Data IDM

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  • Mahmud Adi Yuwanto,
  • Arif Senja Fitrani,
  • Rohman Dijaya,
  • Uce Indahyanti
Mahmud Adi Yuwanto
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Arif Senja Fitrani
Universitas Muhammadiyah Sidoarjo, Indonesia
Rohman Dijaya
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Uce Indahyanti
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Categories

Published 2025-06-20

Keywords

  • Voter Participation ,
  • Village Development Index,
  • Decision Tree,
  • Machine Learning,
  • Mataraman

Copyright (c) 2025 Mahmud Adi Yuwanto, Arif Senja Fitrani, Rohman Dijaya, Uce Indahyanti

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Decision Tree Analysis for Predicting Voter Participation Using IDM Data: Analisis Pohon Keputusan untuk Memprediksi Partisipasi Pemilih Menggunakan Data IDM. (2025). Indonesian Journal of Cultural and Community Development, 16(2), 10.21070/ijccd.v16i2.1255. https://doi.org/10.21070/ijccd.v16i2.1255

Abstract

General Background: Voter participation serves as a core indicator of democratic quality and civic awareness. Specific Background: In East Java’s Mataraman region, significant disparities in electoral participation highlight socioeconomic influences measurable through the Village Development Index (IDM). Knowledge Gap: No prior research integrates IDM-based indicators with machine learning methods for voter behavior prediction. Aims: This study develops a classification model using C4.5, Naïve Bayes, and SVM algorithms to predict voter participation based on IDM attributes. Results: The Decision Tree C4.5 algorithm achieved the highest accuracy (80.87%) and F1-score (0.88) compared to Naïve Bayes and SVM, identifying education and healthcare access as primary determinants of high participation. Novelty: The integration of IDM and C4.5 classification introduces a novel framework for data-driven political participation analysis. Implications: The model can assist policymakers and electoral bodies in targeting civic engagement initiatives within underrepresented regions.

  • Highlights:

  1. C4.5 algorithm effectively predicts voter engagement.

  2. Education and health access influence participation.

  3. Data-driven policy enhances democratic quality.

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