Segmentasi Buah Mangga Menggunakan MLE dan GMM Sebagai Klasterisasi Pixel

Abstract

Segmentation divides an image into parts or segments that are simpler and more meaningful so they can be analyzed further. The solution that has been found is using the Maximum Likelihood Estimation (MLE) method and the Gausian Mixture Model. GMM is a clustering method. GMM is a function consisting of several Gaussian, each identified by k ∈ {1, ..., K}, where K is the number of clusters in our dataset. Maximum Likelihood estimation is a technique used to find a certain point to maximize a function, this technique is very widely used in estimating a data distribution parameter. Tests carried out using mango images with 10 different backgrounds. GMM will cluster the pixels of the mango image to produce averages and covariates. Then the average and covariance will be used by MLE to qualify each pixel of the mango image. In this study GMM and MLE tests were carried out to segment mangoes. Based on the results obtained, the GMM and MLE methods have  an error rate of 13.07% for 3 clusters, 8.06% for 4 clusters, and 6.63% for 5 clusters and good cluster quality with silhouette coefficient values ​​of 0.37686 for 3 clusters, 0.29577 for 4 clusters, and 0.26162 for 5 clusters.