A Hybrid DEMATEL-EDAS Based on Multi-Criteria Decision-Making for A Social Aid Distribution Problem

Irvanizam, Irvanizam; Siraturahmi, Mega; Irwansyah, Aisyah Muthmainnah Binti; Nasir, Puti Finalia; Zulfan, Zulfan; Salwa, Nany

doi:10.1109/cyberneticscom55287.2022.9865435

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Therefore, ISL sign language recognition still has much room for improvement and future studies, such as recognition based on sign language words or an application for real-time recognition. In addition, improving weights in backpropagation iterations can be effectively constructed using decision-making methods such as an integrated objective weighting [38], a hybrid bipolar neutrosophic-EDAS approach [39], and improved trapezoidal fuzzy neutrosophic sets [40][41] [42].…”

Section: Conclusion and Future Studiesmentioning

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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