Algoritma K-Nearest Neighbors (KNN) untuk Klasifikasi Citra Buah Pisang dengan Ekstraksi Ciri Gray Level Co-Occurrence

Rifqi Syahrul Ilhamy; Ucta Pradema Sanjaya

doi:10.61769/telematika.v17i2.525

Authors

Rifqi Syahrul Ilhamy Universitas Nahdlatul Ulama Sunan Giri Bojonegoro
Ucta Pradema Sanjaya Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

DOI:

https://doi.org/10.61769/telematika.v17i2.525

Keywords:

banana, feature extraction, classification, GLCM, KNN

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.

Author Biographies

Rifqi Syahrul Ilhamy, Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Informatics Engineering Study Program

Ucta Pradema Sanjaya, Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Informatics Engineering Study Program

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