Indonesian Journal of Cultural and Community Development

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

Prediction Model of Voter Participation Using Naïve Bayes and Village Development Indicators: Model Prediksi Partisipasi Pemilih Menggunakan Naïve Bayes dan Indikator Pembangunan Desa

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  • Husnul Abidin,
  • Arif Senja Fitrani,
  • Hamzah Setiawan,
  • Uce Indahyanti
Husnul Abidin
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Arif Senja Fitrani
Universitas Muhammadiyah Sidoarjo, Indonesia
Hamzah Setiawan
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Uce Indahyanti
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Categories

Published 2025-06-06

Keywords

  • Election Participation,
  • Naïve Bayes,
  • Village Development Index,
  • Data Mining ,
  • Sidoarjo

Copyright (c) 2025 Husnul Abidin, Arif Senja Fitrani, Hamzah Setiawan, Uce Indahyanti

Creative Commons License

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

How to Cite

Prediction Model of Voter Participation Using Naïve Bayes and Village Development Indicators: Model Prediksi Partisipasi Pemilih Menggunakan Naïve Bayes dan Indikator Pembangunan Desa. (2025). Indonesian Journal of Cultural and Community Development, 16(2), 10.21070/ijccd.v16i2.1243. https://doi.org/10.21070/ijccd.v16i2.1243

Abstract

Background: Electoral participation reflects the quality of democracy, particularly in rural communities with diverse socioeconomic structures. Specific Background: In Sidoarjo Regency, disparities in participation levels among villages suggest that local development factors play a crucial role. Knowledge Gap: Previous models only used demographic attributes without integrating the multidimensional Village Development Index (IDM) indicators. Aims: This study aims to construct a predictive model of voter participation using the Naïve Bayes classification algorithm based on IDM data. Results: By applying preprocessing, feature selection, and probabilistic classification to 48 attributes of IDM, the model achieved 78.65% accuracy, 79% precision, 76% recall, and 77% F1-score, revealing that education, health, and accessibility variables are key predictors. Novelty: Unlike prior research, this work combines social, economic, and ecological IDM dimensions with an open-source Python-based approach for transparent model validation. Implications: The findings demonstrate the feasibility of data-driven governance tools for mapping electoral participation and can support strategic planning to improve civic engagement in rural Indonesia.

Highlights:
• Uses IDM indicators to predict election participation
• Naïve Bayes model achieves 78.65% accuracy
• Supports data-driven democratic planning

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References

  1. [1] A. Sentimen, A. Pemilu, and A. N. Bayes, “SWADHARMA (JEIS),” vol. 5, pp. 131–139, 2025.
  2. [2] Y. Raharja, A. S. Fitrani, and R. Dijaya, “Klasifikasi Tingkat Partisipasi Pemilu Berdasarkan Sektor Industri Menggunakan Algoritma Naïve Bayes,” J. Tekinkom (Teknik Inform. dan Komputer), vol. 7, no. 1, pp. 135–143, 2024, doi: 10.37600/tekinkom.v7i1.1204.
  3. [3] D. E. Safitri and A. S. Fitrani, “Implementasi Metode Klasifikasi Dengan Algoritma Support Vector Machine Kernel Gaussian RBF Untuk Prediksi Partisipasi Pemilu Terhadap Demografi Kota Surabaya,” Indones. J. Bus. Intell., vol. 5, no. 1, pp. 36–44, 2022, doi: 10.21927/ijubi.v5i1.2259.
  4. [4] B. A. B. III, “Digilib UIN Surabaya Repository,” pp. 66–90.
  5. [5] T. Madjid et al., “Peringkat Indeks Desa Membangun,” p. 871, 2020.
  6. [6] H. Rusli, “Prediksi Hasil Pemilihan Umum Berdasarkan Data Media Sosial Menggunakan Teknik Data Mining Naïve Bayes,” JSSR, 2025. [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
  7. [7] A. S. Fitriani, “Penerapan Data Mining Menggunakan Metode Klasifikasi Naïve Bayes Untuk Memprediksi Partisipasi Pemilihan Gubernur,” JTAM (J. Teori dan Apl. Matematika), vol. 3, no. 2, pp. 98–104, 2019, doi: 10.31764/jtam.v3i2.995.
  8. [8] M. N. Zarti, E. Sahputra, A. Sonita, and Y. Apridiansyah, “Application of Data Mining Using Naïve Bayes Method to Predict Public Interest in the 2024 Elections,” J. Kom., vol. 3, no. 1, pp. 105–114, 2024, doi: 10.53697/jkomitek.v3i1.
  9. [9] S. Pokhrel, “No Title,” Agahi, vol. 15, no. 1, pp. 37–48, 2024.
  10. [10] D. Syahputra, “Implementasi Metode Klasifikasi Naïve Bayes Dalam Menentukan Produk Laptop Terlaris,” 2022.
  11. [11] A. W. Anggraeni, A. S. Fitrani, and A. Eviyanti, “Penerapan Algoritma Support Vector Machine Untuk Memprediksi Tingkat Partisipasi Pemilu Terhadap Kualitas Pendidikan,” Edumatic J. Pendidik. Inform., vol. 8, no. 1, pp. 21–27, 2024, doi: 10.29408/edumatic.v8i1.24838.
  12. [12] F. Ratnawati, “Pemanfaatan Adobe Flash Sebagai Dasar Pengembangan Bahan Ajar Fisika,” INOVTEK Polbeng Ser. Inform., vol. 3, no. 1, pp. 50–58, 2018.
  13. [13] D. M. Chulloh, A. S. Fitrani, I. R. I. Astutik, and A. Eviyanti, “Uji Akurasi K-Means Dalam Prediksi Partisipasi Pemilu Kabupaten Pasuruan,” Jutisi J. Ilm. Tek. Inform. Sist. Inform., vol. 13, no. 1, p. 201, 2024, doi: 10.35889/jutisi.v13i1.1753.
  14. [14] D. Pilendia et al., “Pemanfaatan Adobe Flash Sebagai Bahan Ajar Fisika: Studi Literatur,” 2020. [Online]. Available: http://ejournal.stkip-mmb.ac.id/index.php/pgsd/login
  15. [15] W. Kurniawati et al., “Analisis Kehadiran Pemilih Di TPS Menggunakan Algoritma C4.5,” J. Electrical Engineering, vol. 1, no. 1, pp. 18–27, 2024, doi: 10.47134/jte.v1i1.2475.
  16. [16] J. Riset and S. Informasi, “Analisis Pola Kehadiran Mahasiswa Menggunakan C4.5,” vol. 2, no. 1, pp. 60–66, 2025.