Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine Dengan Fitur Local Binary Pattern

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.

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Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2176104 ◽

2021 ◽

pp. 418-426

Author(s):

Suhas S ◽

Dr. C. R. Venugopal

Keyword(s):

Support Vector Machine ◽

Image Retrieval ◽

Classification Accuracy ◽

Kernel Functions ◽

Polynomial Kernel ◽

Support Vector ◽

Svm Classifier ◽

Mr Images ◽

Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

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Detection and Classification of Diseases of Banana Plant Using Local Binary Pattern and Support Vector Machine

2019 2nd International Conference on Signal Processing and Communication (ICSPC) ◽

10.1109/icspc46172.2019.8976582 ◽

2019 ◽

Author(s):

Akshaya Aruraj ◽

Ashish Alex ◽

M.S.P Subathra ◽

N.J Sairamya ◽

S. Thomas George ◽

...

Keyword(s):

Support Vector Machine ◽

Local Binary Pattern ◽

Support Vector ◽

Banana Plant ◽

Classification Of Diseases

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Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i1.2615 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-9

Author(s):

Pulung Nurtantio Andono ◽

Eko Hari Rachmawanto

Keyword(s):

Local Binary Pattern ◽

Training Data ◽

Polynomial Kernel ◽

Support Vector ◽

Gaussian Kernels ◽

Central Java ◽

Testing Data ◽

Different Color ◽

Occurrence Matrix

Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.

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ANALISIS SENTIMEN PENGGUNA GOPAY MENGGUNAKAN METODE LEXICON BASED DAN SUPPORT VECTOR MACHINE

KOMPUTEK ◽

10.24269/jkt.v3i2.270 ◽

2019 ◽

Vol 3 (2) ◽

pp. 52

Author(s):

Rachmad Mahendrajaya ◽

Ghulam Asrofi Buntoro ◽

Moh Bhanu Setyawan

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Open Access ◽

Polynomial Kernel ◽

Support Vector ◽

Linear Kernel ◽

Negative Comments

Go-Pay is part of the Gojek application and one of the most popular finteches in Indonesia. Although the most popular, not all users have positive or even negative comments. Now users can submit various media opinions, one of which is Twitter. Twitter media has the advantage of a simple display, updated topics, open access to tweets and express opinions quickly. From a variety of comments on Twitter it takes a technique to divide into classes positive or negative opinions. This study uses prepocessing and labeling opinions into positive and negative classes with the lexicon Based method. As for the classification using the Support Vector Machine (SVM) method. The data used in the form of opinions about Go- Pay reviews from social media Twitter, amounting to 1210. The results of labeling with Lexicon Based amounted to 923 for positive and 287 for negative. While the classification of the SVM method using the Linear kernel produces 89.17% and 84.38% for the Polynomial kernel.

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