Penerapan Metode Linear Discriminant Analysis Dalam Mendeteksi Kematangan Buah Tomat - Setiawan, Adil; Sumijan, S (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2025)
Tomatoes are fruits of various shapes and sizes originating from the American continent, the tree can reach a height of 2.5 meters, grows as a fruit plant in fields, yards or is found wild at an altitude of 1-1600 meters. above the sea level. Tomatoes are classified as fruits because they are the edible part of the plant and contain seeds or seeds visible through fiber which play an important role in improving digestion. Fiber facilitates bowel movements and prevents constipation, thereby improving the digestive system. Researchers examined the level of ripeness of tomatoes to obtain a classification of ripe tomato parts and unripe tomato parts by utilizing the Linear Discriminant Analysis method, one of the supervised learning algorithms used to carry out classification in machine learning. This technique is used to find the best linear combination of variables as an indicator of separating classes (classifying) in the dataset. LDA works by projecting data into a lower dimensional space that maximizes the separation between RGB, hue, saturation classes. Farmers usually sort them manually to determine the ripeness of tomatoes, so a system is needed that is capable of classifying the ripeness of tomatoes using the Linear Discriminant Analysis method. Based on the results of accuracy testing, the accuracy rate reaches 85%, this is a result that has become reference material for future researchers