Bornean Orangutan Nests Classification using Multiclass SVM

Bornean orangutans are critically endangered, and experts are working to manually spot and classify their nests. This is a challenging task, as it requires a deep understanding of orangutan behavior and their natural habitats. To address this challenge, researchers are developing advanced image analysis techniques that can minimize human intervention. In this paper, we propose a method for classifying orangutan nests using Support Vector Machines (SVMs). SVMs are a powerful machine learning algorithm that has been shown to be effective for image classification. Our method includes a comprehensive image pre-processing and enhancement pipeline, as well as a rigorous training and testing procedure. The results of our study show that SVMs can achieve high accuracy in classifying orangutan nests. We achieved an accuracy of 79.90%, a F1-score of 79.87%, a precision of 79.91%, and a recall of 79.82%. These results demonstrate the effectiveness of our method and the potential of SVMs for this task.