Aplikasi Citra Digital untuk Klasifikasi Kematangan Buah Pepaya - Ulfa Laela R; Asriayani Ismail; Raden Wirawan; Najirah Umar; Rahmat Hidayat (Prosiding SISFOTEK, 2024)
There are still many people who use the manual system by looking directly at or guessing the maturity level of papaya fruits, with classification results that are less than optimal. The purpose of this study is to design a digital image application that can be useful in distinguishing the ripeness of naturally ripe papaya fruits and carbites. This application uses 2 extraction features, namely texture and colour, for texture uses Grey Level Cooccurence Matrks (GLCM), while colour uses Hue, Saturation, Value (HSV) and K-Nearest Neighbour (K-NN) algorithm to classify. The result of this study is the creation of a digital image application, which can help distinguish the ripeness of papaya fruit from the results of the classification of natural ripe and ripe carbine. Based on the test results, 60 training data and 15 testing data were used. By producing an accuracy value of 80% on each testing data that has been carried out.