Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image

Abstract

Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.

Classification of Traffic Vehicle Density Using Deep Learning

“…CNN is often used to recognize objects or landscapes and performs object detection and segmentation. CNN learns directly from its image data thereby eliminating feature extraction manually [8]. The overview of CNN architecture can be shown in Figure 1.…”

“…The activation function is calculated after convolution operation. Activation functions that are often used in convolutional neural networks include tanh(), ReLu (Reactified Linear Unit), sigmoid, and softmax [8]. This research will be use ReLu and SoftMax activation functions.…”

“…Output from pooling operation is a matrix with smaller dimensions compared to the initial image. Convolution and pooling process is carried out to obtain the desired feature map to be input into fully connected layer [8]. The pooling illustration is shown in Figure 2, namely pooling by max-pooling.…”

“…If the stride value is 1, the convolution filter will shift by 1 pixel horizontally and vertically. More smaller stride value, model will capture more detailed information from an input image, but it requires more computation when compared to a large stride [8]. A small stride value does not always produce better pixel information details, but with a small stride value prevents stacking of unused pixel information.…”

“…But if you add zero padding with a value of 1x1, then the resulting map feature is 3x3 (more information is generated). Calculating the dimensions of a feature map can be used the following equation (8). (8) Where: V = Volume Size F = Filter height P = Zero Padding S = Stride…”