Gorontalo Medicinal Plants Image Identification System Using Artificial Neural Network with Back Propagation

Abstract

The purpose of this research is to design the application of digital image processing system to identify the image of medicinal plants of Gorontalo region using artificial neural network method using back propagation. This research used a digital image processing method with segmentation and extraction techniques. Segmentation process was carried out using thresholding method. Furthermore, a process of characteristic extraction from medicinal plants drawings was carried out using feature and color feature extractions to obtain the value of metric, eccentricity, hue, saturation and value. these five values were used as parameters for input neurons and one output neuron which denoted the class of the medicinal plants image. Data of this research consisted of 91 images which had been divided into two types, training data and test data. The training data consisted of 80 images and the test data consisted of eleven images. A network architecture was obtained from the training result and it provided the highest accuracy level (100%) and least number of iteration with a number of 50 neurons on hidden layer and 143 epochs. The testing result showed a lower accuracy of 54.54%.

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“…As for the 2 hidden layers with a combination of 15 and 35 neurons, the accuracy is 79.89%. Another research regarding the development of applications that are used to identify the image of medicinal plants in the Gorontalo area using an artificial neural network using backpropagation (Latief & Yusuf, 2018). From the evaluation results, the network architecture with the highest accuracy reaches 100% and the least number of iterations with 50 neurons in the hidden layer and 143 epochs.…”