Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm

Borman, Rohmat Indra; Rossi, Farli; Jusman, Yessi; Rahni, Ashrani Aizzuddin Abd; Putra, Syahrizal Dwi; Herdiansah, Arief

doi:10.1109/ice3is54102.2021.9649677

Cited by 19 publications

(13 citation statements)

References 16 publications

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“…Tercatat hampir 10% atau sekitar 350.000 spesies dari seluruh tumbuhan berpembuluh dapat digunakan sebagai obat [3]. Tanaman obat yang sudah dikenal di dunia mencapai sekitar 40.000 jenis obat herbal dan sekitar 30.000 tanama obat tersebut diyakini terdapat di Indonesia [4]. Seluruh bagian tumbuhan dapat digunakan sebagai obat, salah satunya adalah daunnya.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk

Herdiansah

Borman²,

Nurnaningsih³

et al. 2022

Jur. Ris. Kom.

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Since ancient times until now herbal plants have been used for treatment and have been applied in the world of health to this day. All parts of the plant can be used as medicine, one of which is the leaves. However, there are still many people who are not familiar with the medicinal leaves. This is because the leaves at first glance look almost the same, making it difficult to tell them apart. Actually, if you look closely, the leaves have characteristics that can be distinguished from one leaf to another. The purpose of this study is to classify images of herbal leaf species using the Backpropagation Neural Network (BNN) algorithm with shape feature extraction utilizing metric and eccentricity parameters. BNN is a type of supervised learning algorithm that consists of several layers and uses an error output as a modifier of the weight value backwards. In this study, the extraction of shape features that become input for the BNN algorithm will go through morphological operations to improve the segmentation results so that the classification results are more optimal. The test results show an accuracy of 88.75%, this shows the developed model can classify herbal leaves well

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Section: Pendahuluanunclassified

Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk

Herdiansah

Borman²,

Nurnaningsih³

et al. 2022

Jur. Ris. Kom.

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“…There are several feature extraction approaches, including first-order feature extraction. This feature extraction performs extraction by utilizing the values in the image histogram [1]. Several studies have shown that feature extraction can help in image classification [15] [16].…”

Section: Literatures Reviewmentioning

confidence: 99%

“…But now mint leaves have been widely cultivated in Indonesia because of the usefulness and benefits needed. However, based on several studies, the leaves have almost the same characteristics, making it difficult to identify [1][2] [3]. Moreover, the mint plant is not native to Indonesia, so the general public is not familiar with this plant [4].…”

Section: Introductionmentioning

confidence: 99%

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

Harjanti

Setiyani

Trianto

et al. 2022

Tech-E

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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% â€‹â€‹and accuracy is 83%.

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“…Pendekatan ekstraksi ciri citra yang dapat digunakan yakni ekstraksi ciri orde pertama. Pendekatan ini akan melakukan ekstraksi berdasarkan karakteristik histogram citra [12]. Terdapat parameter-parameter yang digunakan dalam ekstraksi ciri orde pertama, diantaranya: mean, skewness, variance, kurtosis, dan entropy.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine

Chusna¹,

Shalahudin²,

Riyanto³

et al. 2022

bits

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Mushrooms are plants that have high nutritional content and have various benefits for the health of the human body. However, not everyone knows the types of mushrooms that are suitable for consumption. The types of mushrooms have their own characteristics when viewed from the image. For this reason, a system is needed by utilizing digital image processing to classify types of mushrooms suitable for consumption, so that people can find out which types of mushrooms are suitable for consumption. This research is to classify types of mushrooms suitable for consumption using the Multiclass SVM algorithm with first-order feature extraction, which performs feature extraction based on the characteristics of the image histogram. The result of feature extraction is used as input for classification in Multiclass SVM. Multiclass SVM can map data points to dimensionless space to obtain hyperplane linear separation between each class. The developed method is implemented in Matlab, in order to produce a system in the form of a GUI so that it can be used by general users easily. Based on the test results, the average accuracy is 83%.

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Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm

Cited by 19 publications

References 16 publications

Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk

Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk

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

Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine

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