Algoritma K-Means Untuk Segmentasi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna - Furqan, Mhd; Sriani, S; Aulia, Atiqah (J-SAKTI (Jurnal Sains Komputer dan Informatika), 2022)
The condition of citrus fruits can be determined by looking at several parameters, one of which is color, larger pores, and even yellow skin. So far, the identification of the maturity level of citrus fruits by farmers and consumers has used manual techniques, for example paying attention to the color, pores and peel of the orange product. Such identification will be very large and fluctuating developmental days because people have visual impairments in recognizing, fatigue, and judgment on great development. Barriers to strategy guidance require innovations that can complete the development process impartially, and with clearer results. One of them is the segmentation process using yahoo k-means. The segmentation process aims to divide or separate the image into several (local) districts based on the specified attributes. The k-means algorithm will cluster data with similar attributes assembled into one set and data with various qualities assembled into different sets. From the results of taking pictures from 6 angles, namely front, back, top, bottom, and right and left using 8 datasets, it produces 48 images, and by testing the clustering results, ripe oranges produce 6 and 2 ripe.