<![CDATA[Abstract. Hermadi I, Wulandari, Dhira D. 2022. Development of a protected birds identification system using a convolutional neural network. Biodiversitas 23: 2561-2569. The protected animals are the animals having small populations, a sharp decline in the number of individuals in the wild, or endemic. The government has banned owning, keeping, or trading these animals. The first step in conserving these animals is identification. The government of the Republic of Indonesia has defined 564 species of bird as protected. This issue becomes a challenge to bird species identification. This study aims to develop a web application that implements a convolutional neural network (CNN) model for image-based protected bird species identification. This study uses the images of ten protected bird species in Indonesia as the research subject. This study consists of the stages viz. data collection, data preprocessing, data splitting, CNN model development, model evaluation, and web development using the Prototyping method. This study has successfully developed a model that gained 97% accuracy, 98% precision, and 97% recall on testing data. The study utilized HTML, CSS, Javascript, and Tensorflow.js for Web development. The black-box testing result shows that the prototype is acceptable.]]>
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