Estimation of Time Voting in Elections Using Artificial Neural Network

Compiler

View Publication Info
 
 
Field Value
 
Title Estimation of Time Voting in Elections Using Artificial Neural Network
 
Creator Hidayati, Nur
Fachrie, Muhammad
Wibowo, Adityo Permana
 
Subject Informatika
Estimation, Time, Election, Multilayer Perceptron
 
Description 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.
 
Publisher Sekolah Tinggi Teknologi Adisutjipto Yogyakarta
 
Contributor
 
Date 2019-11-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ejournals.stta.ac.id/index.php/compiler/article/view/499
10.28989/compiler.v8i2.499
 
Source Compiler; Vol 8, No 2 (2019): November; 131-138
2549-2403
2252-3839
10.28989/compiler.v8i2
 
Language ind
 
Relation http://ejournals.stta.ac.id/index.php/compiler/article/view/499/pdf
http://ejournals.stta.ac.id/index.php/compiler/article/downloadSuppFile/499/61
 
Rights Copyright (c) 2019 Compiler
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library