Performance Analysis of the Combined K–Nearest Neighbor (KNN) and Principal Component Analysis (PCA) Algorithms in Bird Species Image Classification - Tawakal, Rayendra (Journal of Advanced Computer Knowledge and Algorithms, 2025)
For most people, learning more about the many types of birds is difficult because there are so many species and many of them look similar in terms of size, color, and shape. Identifying bird species is not an easy task since it requires special skills, time, and money to study each type. Therefore, this study aims to develop an image processing system to classify bird species, especially birds found in the Aceh region. The system uses a combination of the K-Nearest Neighbor (K-NN) algorithm and Principal Component Analysis (PCA). Feature extraction in this study is based on the color and shape of the birds. The K-NN algorithm groups objects by finding the closest distance between them. Meanwhile, PCA is used to reduce the size of the data while keeping most of the important information. Based on the test results, the system achieved an accuracy of 82.50%, a precision of 83.06%, and a recall of 82.50%. This shows that combining K-NN and PCA in classifying bird images can produce better accuracy than using only the K-NN algorithm.Bird Species