Classification of Perkutut Bird Using the KNN Algorithm with RGB Features

Simon Tanama, Muhamad Minanul Lathif, Christy Atika Sari

Abstract


Indonesia has 1,539 bird species, representing 17% of the world's total bird species, with 381 being endemic. Keeping birds, especially Perkutut birds (Geopelia striata), is popular in Indonesia due to their melodious voices and elegant appearance. However, the challenge of accurately identifying Perkutut species remains a problem for enthusiasts due to the similarity in feather colours and patterns among species. This study proposes a solution using the K-Nearest Neighbors (KNN) algorithm with RGB (Red, Green, Blue) feature extraction from Perkutut bird images. This study aims to help bird enthusiasts distinguish Perkutut species more efficiently and accurately. The research methodology involves collecting 315 Perkutut bird images, preprocessing data such as background removal and resizing, and extracting RGB colour features for classification using KNN. The KNN model is evaluated using accuracy metrics, and an accuracy rate of 85% was achieved in classifying Perkutut bird images. Despite some classification errors due to similar colour patterns among species, the model's performance demonstrates the potential of machine learning in bird species identification. Further development is recommended to increase the amount of training data, improve preprocessing techniques with data augmentation, and consider additional features such as texture or shape to enhance model accuracy. Exploring other algorithms, such as SVM or CNN, and developing practical applications for users are also suggested for further development


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