Ballot Counting System Prototype with Hadoop Platform using Map and Reduce

Abstract

Indonesia is among the nations practicing a democratic system, where the head of state is elected through general elections. During these elections, every citizen possessing a National Identity Card (KTP) holds equal voting rights. Counting votes accurately amidst the vast number of ballots poses a challenge. The ultimate objective is to develop a vote counting implementation using Hadoop, utilizing a form C1 image as input. The numerical data from the C1 form is processed into INT data types employing artificial neural networks (ANN) generated through the Restricted Boltzmann Machine (RBM), trained with MNIST data. These ANN models are uploaded onto a website for image storage in the database. The website's data is then crawled using Apache Nutch, and the obtained results are processed through Hadoop's MapReduce algorithm. Testing reveals efficient performance, with the recapitulation of results using Apache Nutch and Hadoop employing multimode and fair scheduler scheduling algorithms taking one minute and twenty-five seconds. Conversely, Hadoop configured with a single-node setup and FIFO scheduling algorithm achieves a quicker time of 67 seconds.