The Integration of HSV and GLCM Features with LDA for Classification of Breadfruit Maturity Levels - Hamdan Pratama; Nurul Khairina; Nanda Novita; Muhammad Huda Firdaus; Yolanda Y.P. Rumapea (Ali Institute or Research and Publication, 2026)
Breadfruit is a perennial plant that has historically been distributed throughout Southeast Asia as a food source. Breadfruit that has entered the harvest period or has fallen on its own has several levels of maturity, namely raw, unripe, ripe, and rotten. Breadfruit that has been separated from the tree will have the same characteristics, namely green and slightly yellowish or brownish in colour. The research problem centres on the trouble buyers and sellers have when determining the maturity level of breadfruit. Based on this problem, the purpose of this study is to classify the maturity level of breadfruit using the LDA method. With image classification, it is hoped that the maturity level of breadfruit can be identified more accurately. The research gap in this study lies in the limited number of feature extraction methods used simultaneously, as well as the infrequent use of LDA methods for classification. In this study, Linear Discriminant Analysis is applied together with GLCM and HSV-based feature extraction. The LDA is a statistical method used for classification. LDA focuses on finding lines that separate two or more classes in a dataset by maximizing the distance between class averages and minimizing variance within classes. GLCM feature extraction is an image-processing technique used to evaluate texture. The contribution of this research lies in its improved classification performance and greater accuracy compared to previous studies. It offers a statistical description of how pairs of gray levels are distributed within an image, helping to reveal texture patterns and characteristics. The results of this study show that the classification of maturity levels in breadfruit images is good. This is measured by an accuracy of 89.9333%, precision of 90.1732%, recall of 89.3333%, and an F1-score of 89.7513%.