Estimation of Time Voting in Elections Using Artificial Neural Network
Submitted : 2019-08-15, Published : 2019-11-01.
Abstract
Since the first election policy was enacted simultaneously, it does not mean that it does not have potential problems, instead it causes other problems, which require extra time and energy in doing recapitulation. Simultaneous elections consist of presidential elections, DPR elections, Provincial DPRDs, City / Regency DPRDs, DPD, the more they are elected, the more influential is the time of voting and the time of vote recapitulation. The longer the voting time is done by the voters, the longer the recapitulation time. The longer time of recapitulation results in the fatigue of KPPS members which triggers inaccurate work and prone to manipulation and fraud so that it can damage the quality of elections. This study aims to determine the estimated time needed for voting for ballots in elections using the Multilayer Perceptron Artificial Neural Network (ANN) approach. The resulting time estimate is based on the time of the voter in the voting booth. The results of this study indicate that ANN with the Multilayer Perceptron Algorithm can calculate the estimated time required for ballot balloting by producing the best combination of learning parameters with 4 hidden neurons, learning rate 0.001, and 2000 epoch iterations resulting in an RMSE value of 108,015 seconds.
Keywords
Full Text:
PDF (Bahasa Indonesia)References
Triono. (2017). Menakar Efektivitas Pemilu Serentak 2019. Jurnal Wacana Politik, 2(2), 156–164.
Prayogo, Y. P., Wintolo, H., & Indrianingsih, Y. (2013). Perancangan dan Penerapan Algoritma Nakula Sadewa untuk Mengatasi Duplikasi Pemilihan di tempat Pemungutan Suara. Jurnal Compiler, 2(2), 1–20.
Agustyati, K. (2016). Syarat Kepesertaan Peserta Pemilu. Jurnal Pemilu Dan Demokrasi, 9(9), 1–17.
Solihah, R. (2018). Peluang dan Tantangan Pemilu Serentak 2019 dalam Perspektif Politik. Jurnal Ilmiah Ilmu Pemerintahan, 3(1), 73–88. https://doi.org/10.14710/jiip.v3i1.3234
Prasetyoningsih, N. (2014). Dampak Pemilihan Umum Serentak Bagi Pembangunan Demokrasi Indonesia. Jurnal Media Hukum, 21(2), 241–263.
Haqqi, R., Marpaung, H. S., & Sebayang, M. (2017). Analisis Waktu Tempuh Kendaraan Bermotor dengan Metode Estimasi Instantaneous Model. Jurnal JOM FTEKNIK, 4(2), 1–8.
Setiawan, D. (2016). Penerapan Jaringan Syaraf Tiruan Untuk Estimasi Needs Office Equipment Menggunakan Algoritma Backpropagation. Jurnal Sains Dan Teknologi Informasi, 2(1), 2–6.
Fachrie, M., & Wibowo, A. P. (2018). Jaringan syaraf tiruan untuk memprediksi kinerja satpam. Jurnal Informatika Dan Komputer, 3(1), 46–51.
Wibisono, G., & Hermawan, A. (2019). FAKTOR-FAKTOR PENENTU GEJALA PENYAKIT KANKER PAYUDARA. Jurnal Aplikasi Sains, Informasi, Elektronika Dan Komputer, 1(1), 1–6. Retrieved from http://jurnal.unmer.ac.id/index.php/jasiek/article/view/3098/pdf
Pamungkas, A. (2017). Cara Menghitung Nilai MSE, RMSE, dan PNSR pada Citra Digital. Retrieved July 14, 2019, from https://pemrogramanmatlab.com/2017/06/04/cara-menghitung-nilai-mse-rmse-dan-psnr-pada-citra-digital/
Waluyo, T., Hermawan, A., & Wibowo, A. P. (2019). PREDIKSI PENJUALAN SEPEDA MOTOR HONDA MENGGUNAKAN JARINGAN SYARAF TIRUAN. Jurnal of Information System Management, 1(1), 31–35.
Article Metrics
Abstract view: 504 times
Download : 259 times
This work is licensed under a Creative Commons Attribution 4.0 International License.
Refbacks
- There are currently no refbacks.