Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image - Nugroho, Bangkit Indarmawan; Aziz, Taufiq; Santoso, Nugroho Adhi; Gunawan, Gunawan (Journal of Intelligent Decision Support System (IDSS), 2024)
Mango is a fruit crop favored by the community, especially the people of Probolinggo. The most widely planted types of mangoes in the Probolinggo area are Saruman is, golek, and manalagi mangoes because they taste good. This study uses mango leaves as a dataset of three types of mangoes: arumanis, golek, and manalagi. Various ways can be done to distinguish mango types, one of which is by looking at the shape and texture of the mango tree leaves. Suppose you look at the data in the field. In that case, the shape and texture of the leaves of Saruman, golek, and manalagi mangoes have many similarities, making it difficult to distinguish with the naked eye. This research aims to classify mango types based on leaf shape and texture using the K-Nearest Neighbor method. The shape feature extraction process uses compactness and circularity methods, while the texture feature extraction process uses energy and contrast from the co-occurrence matrix approach. The classification method used is K-Nearest Neighbor. The test results of shape feature extraction took 0.043 seconds and texture 0.053 seconds