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Estimation of Time Voting in Elections Using Artificial Neural Network Hidayati, Nur; Fachrie, Muhammad; Wibowo, Adityo Permana
Compiler Vol 8, No 2 (2019): November
Publisher : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (258.048 KB) | DOI: 10.28989/compiler.v8i2.499

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.
Tunisian Political Revolution: A Lesson Learned for Recent Indonesian Politics in Using Social Media Fachrie, Muhammad
Politea Vol 3, No 1 (2020): Politea : Jurnal Pemikiran Politik Islam
Publisher : IAIN Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1051.967 KB) | DOI: 10.21043/politea.v3i1.7147

Abstract

This research discusses about Tunisian Revolution and a lesson learned for recent Indonesian Politics. The fall of Ben Ali is a proof that social media can be a non-military weapon for society in ruining the ruling regime. Social media can create public sphere for Tunisian people in communicating each other, sharing information and even mobilizing the protest over Ben Ali’s regime. This experience gives lesson learned for many country, including Indonesia. Recently, Indonesian people are active in internet, particularly social media, so that Tunisian Revolution experience alerts Indonesian people to always pay attention about that. This research uses qualitative method to gather data and describe the phenomenon by using Critical Theory. This research views that Tunisian experience influences to the government in managing protests in Indonesia, because the policy and regulation about internet and social media occur in Joko Widodo’s administration in order to counter hoaxes, SARA and radicalism. 
Estimation of Time Voting in Elections Using Artificial Neural Network Hidayati, Nur; Fachrie, Muhammad; Wibowo, Adityo Permana
Compiler Vol 8, No 2 (2019): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v8i2.499

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.