A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor

Jatmoko, Cahaya; Sinaga, Daurat

doi:10.33633/jais.v3i2.2151

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…e support of classification methods is essential for successful classification of imbalanced data sets, and a good classification method is the key to success. When traditional classification methods are used to classify imbalanced data sets, however, test samples from a few classes are frequently misclassified into the majority of classes, making it easy to overlook a few classes and resulting in poor classification performance [6]. Because the complex distribution of unbalanced data is difficult to capture, current technology in this field still has many flaws.…”

Section: Introductionmentioning

confidence: 99%

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

Ji¹

2022

Scientific Programming

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The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.

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Section: Introductionmentioning

confidence: 99%

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

Ji¹

2022

Scientific Programming

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“…Some algorithms are also combined with GLCM to obtain good results [23], [24]. The introduction of Indonesian batik patterns using the GLCM method and PCA feature extraction [25].…”

Section: Introductionmentioning

confidence: 99%

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Sasongko

Jayanti²,

Ariessaputra³

2022

JOIV : Int. J. Inform. Visualization

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Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.

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A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor

Cited by 2 publications

References 10 publications

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

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