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Algoritma K-Nearest Neighbors (KNN) untuk Klasifikasi Citra Buah Pisang dengan Ekstraksi Ciri Gray Level Co-Occurrence
Abstract
Banana is one type of fruit that is rich in benefits. Bananas have a soft flesh texture. There are various sizes of bananas based on the type. The color and shape of bananas differentiate one type of banana from another. This research recognizes and classifies bananas based on their skin color by using digital image processing. The gray level co-occurrence matrix (GLCM) feature is an extraction technique commonly used to find features in an image. The classification technique in this study uses the k-nearest neighbors (KNN) algorithm. This study obtained the best accuracy of 76.6% at an angle of 0 degrees with a value of k = 1, 90% accuracy at an angle of 45 degrees with a value of k = 1, 86% accuracy at an angle of 90 degrees with a value of k = 1, and 73% accuracy at an angle of 135 degrees with a value of k = 5, 7, 9.
Pisang merupakan salah satu jenis buah yang kaya manfaat. Pisang memiliki tekstur daging yang lembut. Ada berbagai macam ukuran pisang berdasarkan jenisnya. Warna dan bentuk pisang menjadi pembeda jenis pisang yang satu dengan yang lainnya. Dengan menggunakan pengolahan citra digital, penelitian ini melakukan pengenalan dan klasifikasi pisang berdasarkan warna kulitnya. Fitur gray level co-occurrence matrix(GLCM) merupakan teknik ekstraksi yang biasa digunakan untuk mencari ciri dari sebuah gambar. Teknik klasifikasi dalam penelitian ini mengunakan algoritma k-nearest neighbors (K-NN). Penelitian ini memperoleh akurasi terbaik sebesar 76,6% pada sudut 0 derajat dengan nilai k = 1, akurasi 90% pada sudut 45 derajat dengan nilai k = 1, akurasi 86% pada sudut 90 derajat dengan nilai k=1, dan akurasi 73% pada sudut 135 derajat dengan nilai k = 5, 7, 9.
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