A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor
| Autor: | Cahaya Jatmoko, Daurat Sinaga |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
Computer science business.industry System evaluation 020206 networking & telecommunications Pattern recognition 02 engineering and technology lcsh:QA75.5-76.95 k-nearest neighbors algorithm Correlation 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Artificial intelligence Texture feature business Test data |
| Zdroj: | Journal of Applied Intelligent System, Vol 3, Iss 2, Pp 96-107 (2019) |
| ISSN: | 2502-9401 2503-0493 |
| DOI: | 10.33633/jais.v3i2.2151 |
| Popis: | In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data. |
| Databáze: | OpenAIRE |
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