Classification of Tempeh Maturity Using Decision Tree and Three Texture Features

Abstract

Tempe is an average food from Indonesia, eaten in Indonesia. Even today, tempe is around the world, and vegans around the world use tempeh as a meat substitute. This study plans to work on the accuracy of tempe characterization by utilizing the three-element extraction technique and the choice tree arrangement strategy. This research uses a decision tree method with three texture features in its classification. The results obtained indicate that this method has the highest Gabor channel level, including extraction, which is 71% accuracy, the split proportion is 10;90 and the lowest is 60% with parted balance of 90:10. The most important level value of GCLM extraction precision is 86% with a split proportion of 90;10 and the lowest price level and 60% level with a split ratio of 10;90 for Wavelet including the highest extraction rate price is 77%. It can be said that from the extraction of three elements, GLCM is the element extraction with the highest value from Gabor and Wavelet, including extraction at a split proportion of 10:90 by 86%. The test shows the Featured Tree highlight designation. The extraction technique was superior to different strategies for interaction characterization of tempe development quality. In the next research, improve the accuracy performance so that it can reach 100% using the CNN deep learning method. Then you can also add Support Vector Machine (SVM) and Naive Bayes methods based on the GLCM Extraction feature.

Keywords

• glcm
• classification
• decision tree
• extraction.