scholarly journals Wayang Image Classification Using SVM Method and GLCM Feature Extraction

2021 ◽  
Vol 4 (2) ◽  
pp. 373-382
Author(s):  
Muhathir Muhathir ◽  
M Hamdani Santoso ◽  
Diah Ayu Larasati
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Gray Level ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Life Values ◽  
The Many ◽  
Occurrence Matrix ◽  
Total Accuracy

Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained.

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 Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna

2020 ◽  
Vol 5 ◽  
Author(s):  
Toni Dwi Novianto ◽  
I Made Susi Erawan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Region Of Interest ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Gray Level ◽  
K Nearest Neighbors ◽  
Eye Color ◽  
Occurrence Matrix ◽  
Fish Eye

<p class="AbstractEnglish"><strong>Abstract:</strong> Fish eye color is an important attribute of fish quality. The change in eye color during the storage process correlates with freshness and has a direct effect on consumer perception. The process of changing the color of the fish eye can be analyzed using image processing. The purpose of this study was to obtain the best classification method for predicting fish freshness based on image processing in fish eyes. Three tuna fish were used in this study. The test was carried out for 20 hours with an eye image every 2 hours at room temperature. Fish eye image processing uses Matlab R.2017a software while the classification uses Weka 3.8 software. The image processing stages are taking fish eye image, segmenting ROI (region of interest), converting RGB image to grayscale, and feature extraction. Feature extraction used is the gray-level co-occurrence matrix (GLCM). The classification techniques used are artificial neural networks (ANN), k-neighborhood neighbors (k-NN), and support vector machines (SVM). The results showed the value using ANN = 0.53, k-NN = 0.83, and SVM = 0.69. Based on these results it can be determined that the best classification technique is to use the k-nearest neighbor (k-NN).</p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Warna mata ikan merupakan atribut penting pada kualitas ikan. Perubahan warna mata ikan selama proses penyimpanan berhubungan dengan tingkat kesegaran dan memiliki efek langsung pada persepsi konsumen. Proses perubahan warna mata ikan dapat dianalisis menggunakan pengolahan citra. Tujuan penelitian ini adalah mendapatkan metode klasifikasi terbaik untuk memprediksi kesegaran ikan berbasis pengolahan citra pada mata ikan. Tiga ekor ikan tuna digunakan dalam penelitian ini. Pengujian dilakukan selama 20 jam dengan pengambilan citra mata setiap 2 jam pada suhu ruang. Pengolahan citra mata ikan menggunakan software matlab R.2017a sedangkan pengklasifiannya menggunakan software Weka 3.8. Tahapan pengolahan citra meliputi pengambilan citra mata ikan, segmentasi ROI (<em>region of interest</em>), konversi citra RGB menjadi <em>grayscale</em>, dan ekstraksi fitur. Ekstraksi fitur yang digunakan yaitu <em>gray-level co-occurrence matrix</em> (GLCM).  Teknik klasifikasi yang digunakan yaitu, <em>artificial neural network</em> (ANN), <em>k-nearest neighbors</em> (k-NN), dan <em>support vector machine</em> (SVM). Hasil penelitian menunjukkan nilai korelasi menggunakan ANN = 0,53, k-NN = 0,83, dan SVM = 0,69. Berdasarkan hasil tersebut dapat disimpulkan teknik klasifikasi terbaik adalah menggunakan <em>k-nearest neighbors</em> (k-NN).</p>

Classification of dental lesions fluorescence spectra using support vector machine method

2005 ◽  
Author(s):  
I. S. Atanasov ◽  
Ekaterina G. Borisova ◽  
O. I. Yordanov ◽  
Tzonko T. Uzunov ◽  
Lachezar A. Avramov
Keyword(s):  
Support Vector Machine ◽  
Fluorescence Spectra ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Dental Lesions

scholarly journals Feature extraction and classification of surface discharges on an ice-covered insulator string during AC flashover using gray-level co-occurrence matrix

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Liu ◽  
Qiran Li ◽  
Boxue Du ◽  
Masoud Farzaneh
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Arc Discharge ◽  
Cold Climate ◽  
Surface Discharge ◽  
Gray Level ◽  
Statistical Parameters ◽  
Surface Discharges ◽  
Occurrence Matrix

AbstractThis study focuses on the feature extraction and classification of surface discharges of ice-covered insulator strings during process of alternating current flashover. The test specimen was the five units suspension ceramic insulators, which was artificially accreted with wet-grown ice in the cold-climate room of CIGELE. Based on the IEEE Standard 1783/2009, flashover experiments were conducted on iced insulators to measure the minimum flashover voltage (VMF) and record the propagating process of surface discharges to flashover by using a high-speed video camera. The gray-level co-occurrence matrix (GLCM) method has been used to extract four parameters of arc discharge images features that characterize different stages of flashover process. The parameters are angular second moment (ASM), contrast (CON), inverse difference moment (IDM) and entropy (ENT). These statistical parameters of GLCM can be extracted to reveal the underlying properties of ice flashover on the insulator surface from the quantitative perspective. The different values of these indicators are representative of the different stages in the process of arc discharge. Once the value of quantitative indicators (ASM, CON, IDM, ENT) of surface discharges exceeds the threshold value, the higher flashover risk of iced insulators will appear. Hence, the proposed methods are helpful to understand and monitor surface discharge on iced outdoor insulator strings for preventing flashover accidents.

scholarly journals Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-119
Author(s):  
Daurat Sinaga ◽  
Feri Agustina ◽  
Noor Ageng Setiyanto ◽  
Suprayogi Suprayogi ◽  
Cahaya Jatmoko
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Correlation Energy ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

scholarly journals Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

2018 ◽  
Vol 970 ◽  
pp. 012012 ◽  
Author(s):  
Arjon Turnip ◽  
M. Ilham Rizqywan ◽  
Dwi E. Kusumandari ◽  
Mardi Turnip ◽  
Poltak Sihombing
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Arrhythmia Detection
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