scholarly journals Pemilihan Daging Kelapa Bermutu Berdasarkan Warna dan Tekstur untuk Produksi Wingko Berkualitas Menggunakan Metode Support Vector Machine (SVM) dan Fusi Informasi

2021 ◽  
Vol 8 (3) ◽  
pp. 587
Author(s):  
Arwin Datumaya Wahyudi Sumari ◽  
Ahmad Alfian Alfian ◽  
Cahya Rahmad
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Imaging System ◽  
Support Vector ◽  
Gray Level ◽  
Single Feature ◽  
Occurrence Matrix ◽  
Selection Of ◽  
Coconut Meat

<p><span>Mutu daging kelapa adalah faktor utama yang menentukan kualitas produksi wingko baik yang berasal dari kelapa muda atau kelapa tua dari varietas genjah. Dalam upaya menjaga kualitas produksi wingko kelapa, diperlukan teknik dalam memilih daging kelapa yang bermutu tinggi secara konsisten dengan bantuan teknologi. Dalam penelitian ini telah dibangun sebuah sistem pencitraan digital berbasis Kecerdasan Artifisial untuk pemilihan daging kelapa bermutu. Pemilihan tersebut didasarkan pada warna dan tekstur dengan memanfaatkan <em>Support Vector Machine</em> (SVM) sebagai pengklasifikasi, dan fusi informasi. Pengolahan citra digital menggunakan kombinasi metode <em>Hue, Saturation, Value (</em>HSV) dan metode <em>Gray-Level Co-Occurrence Matrix</em> (GLCM) sebagai pengekstraksi fitur warna dan fitur energi. Kedua macam fiur tersebut difusikan menjadi fitur tunggal guna mempercepat klasifikasi oleh SVM sebagai landasan pemilihan daging kelapa. Dengan menggunakan sistem ini, pemilihan daging kelapa bermutu berhasil mencapai akurasi sebesar 50%. Dalam penelitian ini juga ditemukan bahwa ketidak tepatan pelabelan memberi dampak signifikan pada akurasi pemilihan daging kelapa.</span></p><p><span><br /></span></p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The quality of coconut meat is a primary factor which determines the quality of wingko production whether that comes from young coconut or old one from Genjah variety. In the effort of maintaining the quality of coconut wingko production, a technique for selecting high quality of coconut meat in consistent way with the aid of technology is needed. In this research, an Artificial Intelligence-based digital imaging system for selecting quality coconut meat has been developed. The selection is based on color and texture by utilizing Support Vector Machine (SVM) as classifier and information fusion. The digital image processing uses the combination of Hue, Saturation, Value (HSV) and Gray-Level Co-Occurrence Matrix (GLCM) methods as color and energy feature extractors. Both features are fused to obtain single feature to accelerate SVM classification as the basis for selection the coconut meat. By using this system, the selection of quality coconut meat is successful to achieve the accuracy as much as 50%. In this research it was also found that incorrectly labeling gives significant impact to the accuracy of coconut meat selection.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

KLASIFIKASI TUMOR JINAK DAN TUMOR GANAS PADA CITRA MAMMOGRAM MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 26 (2) ◽  
pp. 176-186
Author(s):  
Lulu Mawaddah Wisudawati
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided ◽  
Health Organization ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Kanker payudara merupakan penyebab utama kematian pada wanita. Data Global Cancer Observatory 2018 dari World Health Organization (WHO, 2020) menunjukkan kasus kanker yang paling banyak terjadi di Indonesia adalah kanker payudara, yakni 58.256 kasus atau 16.7% dari total 348.809 kasus kanker. Mamografi merupakan teknik yang paling umum digunakan dalam mendeteksi tumor payudara menggunakan sistem sinar-X dosis rendah. Ada beberapa tipe abnormalitas dalam citra mammogram, yaitu mikrokalsifikasi dan massa. Penelitian ini bertujuan untuk meningkatkan performa sistem Computer-Aided Diagnosis (CAD) dalam mengklasifikasi tumor jinak dan tumor ganas dengan mengembangkan metode ekstraksi fitur menggunakan Gray Level Co-Occurrence Matrix (GLCM) dan metode klasifikasi menggunakan Support Vector Machine (SVM). Uji coba dilakukan dengan menggunakan database DDSM dengan 256 citra abnormal (95 tumor jinak dan 161 tumor ganas) menghasilkan nilai akurasi sebesar 83.59% dengan nilai sensitivitas dan spesifisitas 87.58% dan 76.84%. Selain itu, didapatkan nilai AUC sebesar 0.98%. Metode tersebut menunjukkan bahwa sistem memberikan hasil performa yang baik dalam mengklasifikasi tumor jinak dan tumor ganas.

Static Signature Verification Based on Texture Analysis Using Support Vector Machine

2017 ◽  
Vol 8 (2) ◽  
pp. 22-32 ◽  
Author(s):  
Subhash Chandra ◽  
Sushila Maheshkar
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Signature Verification ◽  
Support Vector ◽  
Gray Level ◽  
Global Level ◽  
First Order ◽  
Svm Model ◽  
Level Information ◽  
Occurrence Matrix

Off-line hand written signature verification performs at the global level of image. It processes the gray level information in the image using statistical texture features. The textures and co-occurrence matrix are analyzed for features extraction. A first order histogram is also processed to reduce different writing ink pens used by signers. Samples of signature are trained with SVM model where random and skilled forgeries have been used for testing. Experimental results are performed on two databases: MCYT-75 and GPDS Synthetic Signature Corpus.

scholarly journals A Novel Framework for Detection of Cervical Cancer

2018 ◽  
Vol 7 (2) ◽  
pp. 26-30
Author(s):  
V. Pushpalatha
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Curvelet Transform ◽  
Uterine Cervical Cancer ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Adaptive Support ◽  
Occurrence Matrix ◽  
Five Phases

Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.

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