Penerapan Algoritma Convolutional Neural Network Pada Identifikasi Foto Hero Game Mobile Legends

Abstract

This study aims to develop a hero photo identification system in the Mobile Legends game using the Convolutional Neural Network (CNN) algorithm. The background of this study is based on the need to speed up and simplify the hero identification process which often requires time and special knowledge. The CNN algorithm was chosen because of its superior ability in pattern recognition and image classification. The research method includes several stages, namely collecting Mobile Legends hero image data, data preprocessing including resizing and normalization, and dividing the data into training, validation, and testing data. The CNN model used consists of several convolution and pooling layers for feature extraction, and a fully connected layer for the final classification. Model training is carried out using a dataset that has been processed with augmentation techniques to increase data variation. The results of this study are that the data used amount to 600 image data divided into 30 classes. By implementing the CNN method, researchers have succeeded in creating a system that can recognize images of mobile legends heroes. Based on the scenario created by the researcher, the highest accuracy is a combination of ReLu activation, Dropout 0.2 and using epoch 20, resulting in an accuracy of 62.17%.