Identifikasi Spesies Reptil Menggunakan Convolutional Neural Network (CNN)

Olvy, Diaz Annesa (2020) Identifikasi Spesies Reptil Menggunakan Convolutional Neural Network (CNN). Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

The diversity of reptiles and their uniqueness has increased the interest of reptile enthusiast in various parts of the world, including Indonesia. This interest does not only come from among the community of the enthusiast, but also from the society due to the lack of knowledge of reptile species, which are too diverse in shape and type, so that it creates interest for most people to know this fauna further in order to increase knowledge. As the digital era develops, it takes artificial intelligence for computer programs to do a human-like job where reptile species can be identified automatically whenever a reptile image is given as input. Machine learning through Deep Learning, especially the Convolutional Neural Network (CNN) method, is needed for computer programs to identify the reptile species that you want to know. This study aims to find the right model to produce high accuracy in the identification of reptile species through input from images obtained manually using a cellphone camera. The image is an image consisting of 3 RGB color channels (Red, Green, Blue). Thousands of images are generated through the Data Augmentation process of the collected images, resulting in tens of thousands of training images. 8 models were tested in this study using the Python programming language. With the use of the Dropout technique which is quite effective, the prediction accuracy of 93% is obtained by this study as the highest accuracy in identifying 14 different reptile species such as crocodiles, lizards, turtles and crocodiles which are divided into the Crocodilia, Squamata and Testudinata Orders. Keywords: Data Augmentation, Species Identification, Convolutional Neural Networks, Python, Reptile

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