Pengenalan Citra Bakuan Seafood menggunakan K-Nearest Neighbors

OMA, Raeda (2025) Pengenalan Citra Bakuan Seafood menggunakan K-Nearest Neighbors. Undergraduate thesis, Universitas Katolik Widya Mandira Kupang.

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Abstract

This study aims to develop a model for the introduction of seafood bakuan images using the K-Nearest Neighbors (KNN) algorithm. The data used in this study consisted of 300 images from six types of seafood, namely Red Fish, Grouper, Baronang Fish, Squid, Crab and Lobster, which were represented in the form of digital images. The test is conducted using Cross Validation with 2 folds to ensure an objective evaluation and produce reliable accuracy values. The results of the tests conducted in this study showed that the model achieved a good score with an accuracy value of 95,6%, Precession of 95,9%, recall of 95,6%, and an F-1 Score of 95,6%. The K-Nearest Neighbors algorithm has been shown to be effective in classifying the recognition of seafood standard images. This research is expected to complement the development of a more sophisticated seafood image recognition system in the future.

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