Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor

Abstract

Tempe is one of the traditional foods in Indonesia which has nutritional content and benefits that are very much favored by all Indonesian people. To determine the maturity of tempe, it is generally done by fermenting it into tempeh using a certain temperature and usually tempe entrepreneurs are done traditionally. But in this way, tempe producers do not know what temperature and humidity are right for tempeh maturity. In this study, researchers used the MATLAB R2018a application with a total data set of 137 raw data, 137 ripe data and 136 rotten data, totaling 410 tempe image data. The purpose of this research is to produce a system that can detect the ripeness of tempe using the KNN (K-Nearest Neighbor) method which is equipped with GLCM texture feature extraction, with extraction of 8 color features, using the PCA (Principal Component Analysis) selection feature. And compare the results with the same method, namely KNN (K-Nearest Neighbor) without using the PCA (Principal Component Analysis) selection feature with the required running time between the two. KNN with PCA selection feature gets an average accuracy value of 80.63% and takes 1.06 seconds. Compared with the same method, namely KNN without using the selection feature, it gets an average accuracy value of 81.67% with a time of 1.18 seconds.