DIGITAL IMAGE CLASSIFICATION OF HERBAL LEAVES USING KNN AND CNN WITH GLCM FEATURES

Dinna Zahirah; Purnawansyah Purnawansyah; Nia Kurniati; Herdianti Darwis

doi:10.52436/1.jutif.2024.5.1.1162

Dinna Zahirah Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia
Purnawansyah Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia
Nia Kurniati Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia
Herdianti Darwis Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.1162

Keywords: Classification, CNN, GLCM, Herbal Leaves, KNN, Preprocessing

Abstract

Geographical position and having a tropical climate make Indonesia known for its abundant biodiversity, one of which is herbal leaves. Indonesia has more than 2039 species that fall into the category of herbal medicinal plants. Herbal leaves are plants that are used as an alternative to natural disease healing. The large number of herbal leaf plants makes it difficult for people to distinguish between herbal plants and non-herbal plants, except when herbal leaf plants bear fruit or bloom. With advances in technology, many studies have been conducted to identify types of herbal plants, one of which is to identify the characteristics of the leaves. In this study, image recognition of herbal leaves was carried out using the K-Nearest Neighbor and Convolutional Neural Network methods with feature extraction of the Gray Level Co-occurance Matrix. By using these 2 methods, the data collected in this study were 480 leaf images which were then divided into 80% testing data and 20% training data. The data used are in the form of Sauropus androgynus and Moringa leaves. Based on the test results, the Convolutional Neural Network method which is suggested in the herbal leaf image classification which has an accuracy value of 96%..

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