<![CDATA[The Identification of Anura (frog) based on pattern and color is a complex problem that takes a long time and costs quite a lot. Therefore, this study uses Machine Learning techniques to develop a frog species recognition model based on pattern and color. Image data of different species, patterns, and colors were taken from reliable sources and then divided into training and test data sets. Deep learning techniques were used to study the visual patterns in frog images and develop classification models that can predict frog species based on their patterns and colors. The test results show that the developed model is a fairly high accuracy and can correctly identify frog species based on their patterns and colors. However, the identification of frogs based on pattern and color may not always be accurate in some cases of the images tested. therefore, Machine Learning techniques must be combined with other frog identification methods. The methods used are Contour, Hough Line Transform, K-Means, and Logistic Regression. This research can assist in the conservation of endangered frog species by being able to identify frog species quickly to monitor frog populations in the wild.]]>
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