Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network

Rizka, Fayyadhila (2021) Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

The beauty of Indonesian women is distinguished by skin color, facial structure, hair color and body posture. For women today trying to look beautiful is a must. The way to make yourself look beautiful can be tricked by using make-up. But not that easy for use make-up because the type of make-up is differentiated based on the basic skin color or undertone, this is the problem for women in using make-up. There are three types of undertones, namely warm, cool and neutral. In previous studies on the classification of batik motifs using the Convolutional Neural Network, the accuracy was obtained at sizes 64x64 = 92.85%, 128x128 = 85% and 256x256 = 80%. Then seen from previous research studies with good accuracy values, a modeling of undertone image classification was made using the Convolutional Neural Network algorithm for knowing the type of undertone, it will make it easier for women to use make-up, namely to determine the appropriate shade based on the type of undertone. The wrist vein color image dataset is required. The dataset used is 36 cool, 32 neutral and 37 warm. Then preprocessing is carried out by homogenizing the image size to 64x64 pixels, then augmentation is carried out on each image by rotating and zooming. At this stage, the dataset will be divided into 9000 images which are divided into 80% training data and 20% testing data. Then it is processed through the convolution and pooling process at the feature learning stage, then the fully connected layer and classification stage where the feature learning results will be used for the classification process based on subclasses. Produces accuracy and training model values reaching 99% with a loss value of 0.0214 and for accuracy from data validation it reaches 99% with a loss value of 0.0239 with model testing results of 99.56%. Keyword : Undertone, Make-up, Convolutional Neural Network

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