Klasifikasi Jenis Mangga Apel Menggunakan Metode K-Means Klustering

Abstract

Introduction: The rapid development of technology is pushing it further, making people more comfortable in many fields, including industry. Mango can be processed into various types of food. Using sweets, various processed products, and various mangoes. One example of the current impact of technology on the industrial sector is the potential for systems to self-study (automatically) like humans. This is a process known as an artificial neural network. Purpose: Knowing and analyzing the classification of mango species using the K-Means Clustering method. Methods: This study uses the K-means clustering method, in which the system is built by applying an artificial neural network to the modeling and extraction of RGB values and standard RGB matrices, circumference, area, length, width, shape, and slenderness. Results: Based on the experimental results, the computation time for the Mango image required for the feature extraction process for each dataset is on average 0.85 seconds, and the computational time for training data on the test data is an average of 0.006 seconds. Conclusion: In this study, it can be concluded that the average computation time of mango image for each dataset is 0.856 seconds. The use of 1 hidden layer is more effective with the highest accuracy and the fastest time than using 2 hidden layers.