Application of The K-Means Clustering Algorithm to Identify Strawberry Fruit Ripe - Rizki, Muhammad; Furqan, Mhd; Sriani, S (Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa, 2024)
Fruit ripeness will usually be determined by several parameters, including size, weight, color characteristics, fragrance, etc. The parameter of fruit ripeness in terms of fruit skin color is one of the important factors in identifying fruit maturity. Segmentation is a method in digital image processing to differentiate objects in an input image. The general classification process is carried out by looking directly at the fruit object. The purpose of this research is to create an analysis in identifying the ripeness of strawberry fruit and designing an application system that can identify the ripeness of strawberry fruit. This application was built with the MATLAB application. The methods used include K-Means Clustering segmentation, labeling and feature extraction. The detection of the type of fruit is carried out using feature matching at the level of shape and color. Before classifying the name of the type of fruit and the level of maturity, the fruit training must be carried out first and then continued with fruit detection and identification of maturity. Based on the results of the strawberry image maturity identification test with six test strawberry images consisting of three types of maturity levels, the results were obtained, namely mature test one and mature test two levels of ripeness and correct identification results, half ripe test one and half ripe test two levels of ripeness and results Correct understanding, raw test one and raw test two mature levels and correct recognition. Meanwhile, the accuracy test results obtained an accuracy value of 100% for identifying the maturity of strawberry images. From the results of the tests carried out, it can be concluded that identification of ripeness in strawberry fruit images was successfully applied using the K-Means Clustering method on images of ripe, half-ripe and unripe strawberries. And from testing the identification of ripeness of strawberry fruit with test data of six images and training data of twelve images, it gave an accuracy result of 100%.