Penerapan Ekstraksi Ciri Statistik Orde Pertama Dengan Ekualisasi Histogram Pada Klasifikasi Telur Omega-3

Liantoni, Febri; Santoso, Agus Adi

doi:10.24176/simet.v9i2.2476

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…First-order feature extraction is a technique for locating features or traits in well-known images. First-order feature extraction is a technique that gathers features from the histogram of the image's visible elements [22]. Mean, skewness, variance, kurtosis, and entropy are the parameters utilized in feature extraction.…”

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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“…Ekstraksi ciri orde pertama merupakan sala satu pendekatan mengenali fitur atau ciri dari citra yang populer. Ekstraksi ciri orde pertama yaitu pendekatan yang mendapatkan ciri berdasarkan pada fitur-fitur yang dari histogram citra [16]. Parameter yang digunakan pada ekstraksi ciri ini diantaranya: mean, skewness, variance, kurtosis, dan entropy.…”

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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“…Pada penelitian ini digunakan mean filter untuk preproses citra mammogram [10]. Penggunaan ekualisasi histogram pernah digunakan peneliti pada penelitian sebelumnya mengenai klasifikasi jenis telur biasa dan telur omega-3 berdasarkan ciri orde pertama [11].…”

Section: Pendahuluanunclassified

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni

Santoso

2020

SINTECH Journal

Self Cite

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In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

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Penerapan Ekstraksi Ciri Statistik Orde Pertama Dengan Ekualisasi Histogram Pada Klasifikasi Telur Omega-3

Cited by 4 publications

References 6 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

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

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

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