Classification of Defects in Arabica Coffee Beans Using the KNN (K-Nearest Neighbor) Method in the Gayo Farming Cooperative, Bebeseb District, Central Aceh
Abstract
This study examines the classification of defects in Arabica coffee beans using the K-Nearest Neighbor (KNN) method at the Koperasi Usaha Tani Gayo, Aceh Tengah. Arabica coffee, with its high economic value, is often subjectively evaluated by farmers using their senses, which is less effective. Therefore, this research employs the KNN method to enhance the accuracy of defect classification in coffee beans. The KNN method, a supervised algorithm, classifies objects based on the categories of their nearest neighbors. This study utilizes color digital images processed with web-tools Teachable Machine and the MNIST dataset. The dataset is divided into three parts: training, validation, and testing. Defect images of coffee beans are classified into 16 classes, such as Full Sour Bean, Full Black Bean, and others. Evaluation shows that the KNN model has high accuracy in classifying coffee bean defects, although it requires significant computational time. The research results are implemented in a mobile application based on Flutter and the Dart programming language, simplifying the process of classifying Arabica coffee bean defects at the Koperasi Tani Gayo, enhancing the quality and efficiency of coffee bean determination.
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Indonesian Journal of Applied Technology, Computer and Science [IJATCos]
Universitas Muhammadiyah Sumatera Utara
Kampus Utama
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Sumatera Utara-20238
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