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

Herdiansah, Arief; Borman, Rohmat Indra; Nurnaningsih, Desi; Sinlae, Alfry Aristo Jansen; Hakim, Rosyid Ridlo Al

doi:10.30865/jurikom.v9i2.4066

Cited by 17 publications

(27 citation statements)

References 13 publications

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“…The results of feature extraction are used for information and input for further processes so that they can be used as parameters in the classification [20]. Feature extraction has the goal of retrieving information from an object in an image so that it can be recognized or identified the differences between one object and another [21]. First-order feature extraction is a technique for locating features or traits in well-known images.…”

Section: First Order Feature Extractionmentioning

confidence: 99%

Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics

Nuraini

Destriana

Nurnaningsih

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Sunflower is an important commodity in agriculture, besides being used as an ornamental plant, sunflower is an oil-producing plant and a source of industrial materials. In Indonesia, sunflower productivity is considered less than optimal, because knowledge and information about sunflowers are still lacking. Therefore, information is needed that can be used as an extension of knowledge about sunflowers itself, especially in Indonesia, which is a tropical region which is an area suitable for the growth of sunflowers. Sunflowers can actually be identified based on recognizable traits. However, the similar shape makes it difficult for some people to distinguish the types of sunflowers. This study aims to classify sunflower images using a first-order feature extraction algorithm using the characteristics of mean, skewness, variance, kurtosis, and entropy which are then used as input to the Multiclass SVM identification algorithm. Data points are mapped to dimensionless space using a Multiclass SVM to produce hyperplane-linear separation between each class. Based on the results of testing the accuracy of the model is able to perform classification with an average accuracy of 79%. These results show that the developed model can classify well.

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Section: First Order Feature Extractionmentioning

confidence: 99%

Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics

Nuraini

Destriana

Nurnaningsih

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Ilustrasi confusion matrix dapat dilihat pada tabel 3. Pada penelitian ini matriks yang digunakan adalah accuracy, precision, recall, dan fi score dengan rumus 1, 2, 3 dan 4 [22].…”

Section: Evaluasiunclassified

Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet-50 untuk Klasifikasi Citra Daging Sapi dan Babi

Efendi

Jasril²,

Sanjaya³

et al. 2022

Jur. Ris. Kom.

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Meat is one of the food ingredients needed by humans. The price of pork is cheaper than beef, which has led to the practice of mixing beef with pork for the purpose of making big profits. In plain view, the difference between beef and pork is not striking, so it is difficult for ordinary people to distinguish between them. In terms of color, pork is paler than beef. In terms of texture, beef is stiffer and tougher than pork. In terms of fiber, beef is clearer than pork, so we need a system that can identify the two types of meat. This study uses the Convolutional Neural Network (CNN) algorithm with the ResNet-50 architecture with 3 types of optimizers such as Stochastic Gradient Descent (SGD), Adam, and RMSprop. The dataset used for training first goes through 2 stages of preprocessing, namely cropping and resizing. The results of the study show that the SGD optimizer can outperform the Adam and RMSprop optimizers with 97.83% accuracy, 97% precision, 97% recall, and 97% f1 score with batch size 32, learning rate 0.01, and epoch 50.

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“…Ekstraksi ciri memiliki tujuan untuk melakukan reduksi data sebenarnya dengan menggunakan analisa terhadap fitur-fitur tertentu yang menjadi pembeda antara pola-pola yang ada pada citra [14]. Ekstraksi ciri citra melibatkan pengambilan informasi dari suatu objek pada citra agar dapat dikenali atau identifikasi perbedaan antara objek satu dengan yang lain [15]. Ekstraksi ciri orde pertama merupakan sala satu pendekatan mengenali fitur atau ciri dari citra yang populer.…”

Section: Data Collectionunclassified

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

Cited by 17 publications

References 13 publications

Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics

Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics

Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet-50 untuk Klasifikasi Citra Daging Sapi dan Babi

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

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