Image Classification of Tempe Fermentation Maturity Using Naïve Bayes Based on Linear Discriminant Analysis

One of the foods in Indonesia that has a lot of nutritional content and benefits, one of which is tempeh. Tempe is usually made by fermenting soybeans with mold under special conditions to become tempeh. In the fermentation process, tempeh producers need to monitor the maturity of the tempeh until it is suitable for consumption. To detect this maturity requires a separate effort, so that an image processing approach is proposed in this study with the support of feature selection. An image allows for various features to be taken, such as texture features using GLCM and various color features including RGB, HSV, LAB, CMYK, YUV, HCL, HIS, LCH. With so many features, it is necessary to do a selection so that computation in its classification becomes efficient. This study aims to classify tempeh fermented images using the Naive Bayes method with Linear Discriminant Analysis (LDA)feature selection for GLCM features and eight color features. Tempe fermentation image is divided into three classes, namely raw, ripe and rotten. Based on the experimental results, the average accuracy in the test is 84.06%. In testing the fastest time is 1.87 seconds and the longest is 2.20 seconds. This shows that the classification of fermented tempeh maturity with Naive Bayes with LDA feature selection can work well.