The Implementation of Backpropagation Artificial Neural Network for Recognition of Batik Lampung Motive

Andrian, Rico; Hermanto, Bambang; Kamil, Raja

doi:10.1088/1742-6596/1338/1/012062

Cited by 9 publications

(10 citation statements)

References 2 publications

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“…Dieste-Velasco [14] developed an application to detect analog electronic circuit faults using the ANN model. Andrian et al [15] attempted to investigate the batik Lampung motive using ANN Backpropagation. They compared the training and testing datasets contain-ing 70:30 percent and 80:20 percent.…”

Section: Related Workmentioning

confidence: 99%

Applying Artificial Neural Network Based on Backpropagation Method for Indonesian Sign Language Recognition

Irvanizam,

Horatius,

Sofyan

2023

IJCDS

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Sign-language is a nonverbal language utilized by hearing loss and speech disorder people to interact and speak with each other. Sometimes people call it a visual-language utilizing hand gestures and changes in hand shape to express intentions and thoughts. In Indonesia, people use two sign language types commonly. They are Indonesian Signal System (ISS) and Indonesian Sign Language (ISL). In this paper, we investigated research on the recognition of ISL alphabets by proposing an application using the Artificial Neural Network algorithm with the principle of the Backpropagation method for dealing with this research. Firstly, we performed preprocessing the image data sequentially by splitting the dataset, labeling the dataset based on the alphabet types, scaling the images into a size of 150x150 pixels, and segmentizing the datasets to eliminate noise in the image to yield color, grayscale, binary segmentations, and image edge detection. Later on, we split the dataset and distributed them into training and testing sets with two different proportions, 70:30 and 80:20 percent. After applying the algorithm, we obtained the highest accuracy at 80:20 data proportions using an epoch of 700, a learning rate of 0.001, a batch size of 25, and some hidden nodes of 100 was 98.41 percent.

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Section: Related Workmentioning

confidence: 99%

Applying Artificial Neural Network Based on Backpropagation Method for Indonesian Sign Language Recognition

Irvanizam,

Horatius,

Sofyan

2023

IJCDS

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“…Backpropagation is a supervised artificial neural network training method [23]. It evaluates the error contribution of each neuron after a set of data has been processed.…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

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

Latumakulita

Astawa

Mairi

et al. 2022

JOIV : Int. J. Inform. Visualization

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Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

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“…Various neural network-based architectures have been developed for numerous applications based on the original model. One of the famous neural networks is BPNN, a machine learning method that has been used for years for many applications [27], [28], [29], and shows a very good performance [30], [31], [32], [33], [34], [35], [36], effective [37], and accurate [38].…”

Section: B Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Sumari¹,

Adinandra²,

Syulistyo³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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A hostile or unfriendly aircraft will mostly fly at low-level altitude or hide behind natural obstacles to avoid Radar detection. One of the ways to detect and recognize while at the same time identifying such aircraft is to perform air observation from the ground. A technique called Visual Aircraft Recognition (VACR) has been practiced in training soldiers to recognize and find an incoming aircraft from a distance using binoculars. Remembering so many types of aircraft have their challenge. To ease the task, we have designed and developed an intelligent military aircraft recognition and identification system using the combination of Back Propagation Neural Networks (BPNN) and Information Fusion to speed up the recognition and identification. We use 13 aircraft features fused into five primary ones as the inputs to the BPNN for the recognition, while the identification uses Hamming Distance to the recognition results. With 155 data consisting of 85 military aircraft and helicopters and 70 civilian aircraft and helicopters and applying the 80:20 scheme for the training and test data, our system can obtain 95.33% and 87% accuracy at the training phase and the test phase. It also succeeds in recognizing and identifying a new military aircraft that is not in the dataset, while the Information Fusion can speed up the recognition and identification by up to 6 seconds. This impacts the acceleration of aircraft recognition and identification.

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The Implementation of Backpropagation Artificial Neural Network for Recognition of Batik Lampung Motive

Cited by 9 publications

References 2 publications

Applying Artificial Neural Network Based on Backpropagation Method for Indonesian Sign Language Recognition

Applying Artificial Neural Network Based on Backpropagation Method for Indonesian Sign Language Recognition

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

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

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