Image Classification of Starlings Using Artificial Neural Network and Decision Tree

Rahman, Aviv Yuniar

doi:10.1109/cyberneticscom55287.2022.9865465

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Furthermore, starlings testing was carried out on LR 0.6, which had an accuracy value of 61%, and LR 0.7 had an [23] showed that starlings were classified using a decision tree with GLCM texture features. The results of the research have an accuracy value of 50%.…”

Section: Fig 3 Highest Accuracy Resultsmentioning

confidence: 99%

“…These tests' results show an accuracy rate of 68% at a 90:10 analogy between training information and examination information [21], [22]. After that, the following research grouped starling views using an artificial neural network and Decision Tree with a GLCM texture extraction feature with accuracy reaching 50% [23], [24]. The results on the way of grouping starlings to assess the accuracy are small.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network

Rahman,

Istiadi,

Hananto

et al. 2023

JOIV : Int. J. Inform. Visualization

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Indonesia is a country that has a diversity of animal species with the top 10 predicate in the world. The population of animal species, including starlings, is very widely known in the country. Starlings currently in Indonesia are diverse, ranging from standard to rare in Indonesia. This starling has its characteristics based on the type, color, sound, etc. In the first problem, the first accuracy performance when using the GLCM texture feature with Artificial Neural Network is 68%. Furthermore, the second problem is the accuracy performance of typing using the GLCM texture feature with a Decision Tree of 50%. This research aims to improve the starling classification system accuracy using Gabor and Wavelet texture features with artificial Neural Networks. Based on testing in the classification of starlings using the GLCM, Gabor, and Wavelet features, the highest degree of precision can, therefore, be concluded to be at the GLCM and Wavelet feature levels. The GLCM and Wavelet level accuracy results reached 83% at a rate of learning 0.9. In the experiments that have been done, the GLCM and Wavelet levels can increase accuracy using Artificial Neural Networks. In the classification process, the type of starlings also shows that the computational time in testing is much faster in producing accuracy values. In addition, the accurate accuracy while testing the starling category also increases.

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Section: Fig 3 Highest Accuracy Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network

Rahman,

Istiadi,

Hananto

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…It uses a 90:10 pattern for training and testing data on neural network classifiers and is assisted by manual segmentation to improve classifier accuracy. The affirmation of textures, shapes, and colors is also prepared in the feature extraction stage to enhance the classifier's performance [22]. However, this study only achieved an accuracy figure of 93%.…”

Section: Artificial Intelligence For Data Classificationmentioning

confidence: 99%

Optimizing Lantana Classification: High-Accuracy Model Utilizing Feature Extraction

Sooai,

Mau,

Siki

et al. 2023

kursor

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As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, white, yellow, purple, and orange, as the primary dataset, which had 411 instances. VGG16 assisted feature extraction in preparing datasets for the data training using three classifiers: decision tree, AdaBoost, and k-NN. 2-fold cross-validation, 5-fold cross-validation, and a self-organizing map are used to help validate each process. The experiment to measure the classifier's performance resulted in a good figure of 99.8% accuracy for 2-fold cross-validation, 100% for 5-fold cross-validation, and a primary dataset of lantana interest that can be accessed freely on the IEEE Data port. This study outperformed other related studies in terms of classifier accuracy.

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Fault Detection of Oil Palm Harvesting Using Yolo V4

Sunggara,

Yuniar Rahman,

Istiadi

2023

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)

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Image Classification of Starlings Using Artificial Neural Network and Decision Tree

Cited by 3 publications

References 8 publications

Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network

Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network

Optimizing Lantana Classification: High-Accuracy Model Utilizing Feature Extraction

Fault Detection of Oil Palm Harvesting Using Yolo V4

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