Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Muhammad Nur Ichsan; Nur Armita; Agus Eko Minarno; Fauzi Dwi Setiawan Sumadi; Hariyady

doi:10.29207/resti.v6i4.4222

Muhammad Nur Ichsan Universitas Muhammadiyah Malang
Nur Armita University of Muhammadiyah Malang
Agus Eko Minarno University of Muhammadiyah Malang
Fauzi Dwi Setiawan Sumadi University of Muhammadiyah Malang
Hariyady University of Muhammadiyah Malang

DOI: https://doi.org/10.29207/resti.v6i4.4222

Keywords: CNN, Deep Learning, Image Classification, Machine Learning, Neural Network

Abstract

Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

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