Convolutional Neural Network (CNN) Method for Classification of Images by Age

Nurtiwi Nurtiwi; Ruliana Ruliana; Zulkifli Rais

doi:10.35877/454RI.jinav1481

Authors

Nurtiwi Department of Statistics, Universitas Negeri Makassar, 90223, Makassar, Indonesia
Ruliana Department of Statistics, Universitas Negeri Makassar, 90223, Makassar, Indonesia
Zulkifli Rais Department of Statistics, Universitas Negeri Makassar, 90223, Makassar, Indonesia

DOI:

https://doi.org/10.35877/454RI.jinav1481

Keywords:

Image Classification, Convolutional Neural Network, Preprocessing

Abstract

Image classification is one of the studies that is currently being developed. The details of the characteristics that must be captured make researchers compete to find the most suitable method for classifying. The Convolutional Neural Network (CNN) algorithm is one of the most superior algorithms in the field of object classification and identification today. With the help of several packages contained in Google Colab for classification, this algorithm is easier to use. In this study, the target case is the age of a person who will be classified using photos or images taken from the internet which are then stored in the form of Google Drive. The research data used is divided into 2 parts, namely for training data as many as 23.440 images, and 10,046 for testing data. Then to facilitate the extraction of features from the features to be identified, the researchers carried out the preprocessing stage, namely grayscale images, and data augmentation. The purpose of this study is to implement the concept of Deep Learning with Convolutional Neural Networks (CNN) in image classification and to determine the level of accuracy of the CNN model in classifying images. After the algorithm is run and the model has been formed, an accuracy of 78.5% is obtained. It can be concluded that the Convolutional Neural Network (CNN) method is good at classifying images

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