Steven Pranata
STMIK Global Informatika MDP

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Mango Segmentation Using MLE and GMM as Pixel Cluster Steven Pranata; Derry Alamsyah
Jurnal Algoritme Vol 1 No 1 (2020): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1135.172 KB) | DOI: 10.35957/algoritme.v1i1.435


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 โˆˆ {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 โ€‹โ€‹of 0.37686 for 3 clusters, 0.29577 for 4 clusters, and 0.26162 for 5 clusters.