Classification of Mint Leaf Types Based on the Image Using Euclidean Distance and K-Means Clustering with Shape and Texture Feature Extraction

Abstract

Mint is a plant that has many benefits and uses. However, some people are not familiar with the types of mint leaves because they cannot tell the difference. Actually, if you look closely, mint leaves have their own characteristic shape and texture. However, most people judge mint leaves to have a shape similar to other leaves so it is difficult to tell them apart. This paper aims to classify the types of mint leaves using the Euclidean distance algorithm and K-Means clustering with shape and texture feature extraction. The K-Means Clustering Algorithm functions as a segmentation so that the image to be classified can be separated from other objects. In the feature extraction process, metric and eccentricity parameters are used. Meanwhile, for texture feature extraction, use the parameters in the Gray Level Co-occurence Matrix (GLCM). Furthermore, the classification process uses the Euclidean Distance algorithm which has a function to represent the level of similarity between two images by taking into account the distance value from the identified image. Based on the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, the precision value is 82%, recal is 84% ​​and accuracy is 83%.

Identification of COVID-19 Based on Features Texture Histogram and Gray Level Co-Occurrence Matrix (GLCM) Using K-Means Clustering Methods in Chest X-Ray Digital Images

“…Another study for classifying mint leaves based on GLCM texture features using K-Means Clustering generated accuracy and sensitivity values of 83% and 82%, respectively [41]. Previous studies showed lower results because mint leaves have a higher feature closeness than CXR images in COVID-19 and normal cases.…”

Novel Knowledge Graph- and Knowledge Reasoning-Based Classification Prototype for OBIA Using High Resolution Remote Sensing Imagery

“…Their combined indices included precision (also known as producer accuracy), Recall (also known as user accuracy), F1-score (F 1 ), overall accuracy (OA), and kappa coefficient (Kappa), which were exploited to evaluate the final classification effectiveness. Precision, Recall, F1-score, OA, and Kappa are often utilized to calculate the accuracy of the classification [32,33], whose calculation formulas are as follows:…”

Klasifikasi Buah Berkhasiat Obat Dengan Algoritme Euclidean Distance Menggunakan Ekstraksi Ciri Bentuk dan Tekstur

“…Pendekatan yang dapat digunakan dalam penyelesaian klasifikasi citra yaitu euclidean distance. Pendekatan atau algoritma euclidean distance meruapakan sebuah pendekatan untuk mencocokan citra dengan melakukan identfikasi apakah citra tersebut memiliki kemiripan atau sejenis [8]. Cara kerja algoritma ini adalah *) penulis korespondensi: Farida Amalya Email: farida_a@staff.gunadarma.ac.id ISSN: 2477-5126 e-ISSN: 2548-9356 Farida Amalya: Klasifikasi Buah Berkhasiat Obat … 68 dengan melakukan pencarian terhadap kemiripan dua atau beberapa citra dengan memperimbangkan nilai jarak dari eucliedan, apabila jaraknya semakin dekat maka citra tersebut berada dalam satu kelas [9].…”