Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Latumakulita, Luther; Astawa, I Nyoman Gede Arya; Mairi, Vitrail Gloria; Purnama, Fajar; Wibawa, Aji Prasetya; Jabari, Nida; Islam, Noorul

doi:10.30630/joiv.6.3.1082

Cited by 3 publications

(4 citation statements)

References 20 publications

(22 reference statements)

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“…+,,-./,0 = 12 13 12 13 42 43 (9) This last step is the assessment method, where this method will identify the accuracy numbers that have been tried. This test method will be the number of accuracies in classifying the starling view.…”

Section: G Evaluationmentioning

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: G Evaluationmentioning

confidence: 99%

“…Starling is one of the rare starlings in Indonesia. Although this bird is worldwide, in Indonesia, what makes it very rare is the color pattern of each type of starling [7]- [9]. Starlings themselves in Indonesia have various styles in each region [10], [11].…”

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

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“…After processing a set of data, each neuron's error contribution has been estimated. The goal of backpropagation is to train a neural network by modifying the weights and accurately mapping all inputs to outputs [28], [29].…”

Section: F Neural Network Backpropagationmentioning

confidence: 99%

Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method

Kurniawan,

Almais,

Hariyadi

et al. 2023

JOIV : Int. J. Inform. Visualization

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Performance allowance is a form of appreciation given by an agency to its human resources. The Office of the Ministry of Religion of Batu City provides performance allowances to civil servants who work in the agency. Several things that affect the provision of performance allowances, such as grade, deduction, taxable income, income tax, and total tax, are used in this study to produce the total gross performance allowances and total performance allowances received. Based on the data obtained, there are some missing data from the parameters of taxable income, income tax, and total tax. This study aims to predict performance allowance when there is missing data. The method used is Neural Network Backpropagation. This study uses 480 data with split data ratios of 50:50, 60:40, 70:30, and 80:20, with epochs 40,000 and a learning rate 0,9. Four types of models used in this study are distinguished based on the number of hidden layers and epochs used. Model A uses two hidden layers to produce the highest accuracy with a 50:50 data split ratio of 65,16%. Model B uses four hidden layers to produce the highest accuracy with a 50:50 data split ratio of 69,34%. Model C uses six hidden layers to produce the highest accuracy with a 50:50 data split ratio of 68,18%. Model D uses eight hidden layers to produce the highest accuracy with a 50:50 data split ratio of 70,90%.

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“…of 73.33%, and the highest 80.00%. This research was continued by research [6], which increased system performance with the lowest validation accuracy of 85% and the highest of 100%. Both studies used the K-Fold Cross Validation Technique to evaluate the model.…”

Section: Introductionmentioning

confidence: 99%

Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network

Latumakulita,

Mandagi,

Paat

et al. 2022

Bull.Socinf.The.App

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Indonesia has a variety of medicinal plants that are efficacious for preventing or treating various diseases. Each region has unique medicinal plants, such as in North Sulawesi, there are many medicinal plants with local names of "Jarak" (Jatropha curcas), "Jarak Merah" (Jatropha multifida), "Miana" (Coleus Scutellarioide), and "Sesewanua" (Clerodendron Squmatum Vahl). This research applies the Convolutional Neural Network (CNN) method to identify the types of medicinal plants of North Sulawesi based on leaf images. Data was collected directly by taking photos of medicinal plant leaves and then using the augmentation process to increase the data. The first stage is conducting training and validation processes using 10-fold cross-validation, resulting in 10 classification models. Evaluation results show that the lowest validation accuracy of 98.4% was obtained from fold-4, and the highest was 100% from fold-2. The third stage was to run the testing process using new data. The results showed that the worst model produced a test accuracy of 80.91% while the best model produced an accuracy of 87.73% which means that the identification model is quite good and stable in classifying types of medicinal plants based on its leaf images. The final stage is to develop a web-based system to deploy the best model so people can use it in real-time

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Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Cited by 3 publications

References 20 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

Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method

Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network

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