Classification of Guava Fruit Types Using Principal Component Analysis and K-Nearest Neighbor Algorithms - Andrean Nugraha, Rezky; Wahyu Hidayat, Eka; Nur Shofa, Rahmi; Eka Wahyu Hidayat, S.T., M.T.; Rahmi Nur Shofa, S.T., M.T. (Generation Journal, 2023)
The maturity level of guava fruit can be determined by looking at various factors. Shape is one of the factors that play a role in identifying certain objects. The classification of guava fruit can be seen from the shape, texture and color. The shape of the guava fruit is quite diverse ranging from round (Round shape) to oval (Pear shape). So a Matlab application was built to determine the type of guava based on its color, shape and texture. K-Nearest Neighbor can classify objects based on learning data that is closest to the object so that the results can be more accurate. Principal Component Analysis (PCA) is a statistical technique for simplifying many-dimensional data sets into lower dimensions (extration features). The combination of K-Nearest Neighbor with Principal Component Analysis produces a fairly high accuracy for determining the type of guava using a total of 45 images and divided into two data including training data with a total of 36 guava data and test data with a total of 9 guava data.