Klasifikasi Identitas Dengan Citra Telapak Tangan Menggunakan Convolutional Neural Network (CNN)

Wita, Deviana Sely; Liliana, Dewi Yanti

doi:10.30872/jurti.v6i1.7100

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…11 Identity Classification with Palm Image Using Convolutional Neural Network (CNN) [23] 2022 Based on the results of research and testing on identity classification research with palm images using Convolutional Neural Network (CNN) with identity data as many as 26 labels on palm images, the accuracy value level is 98%, precision is 98%, recall is 99%, and F1-score is 98%. With this accuracy value, it can be concluded that the classification of identity with palm images using the CNN method has a good accuracy value.…”

Section: Summary Of Research Literature Reviewmentioning

confidence: 99%

Review of Deep Learning Using Convolutional Neural Network Model

Kurniawan,

Erlangga,

Tanjung

et al. 2024

Engineering Headway

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Machine Learning can be used to process a lot of data and learn patterns from that data to predict the future. One of the most widely used parts of machine learning is Deep Learning. The Deep Learning method that currently provides the most significant results in image recognition is Convolutional Neural Network (CNN). Convolutional Neural Network (CNN) is one of the deep learning algorithms used for computer vision use cases such as image or video classification and detecting objects within images or even image areas. Some research related to the CNN model states that this model has a very good accuracy of 92% but with a fairly small amount of data and the use of epochs, namely 100, resulting in a higher validation error value than the error value in the training process, so that over fitting will occur. Based on several problems in the related research literature, this article aims to identify the weaknesses and shortcomings of Deep Learning algorithms using CNN models that refer to the state of the art, so that they can be used as a reference for further research. The state of the art related to research in the last five years, the Deep Learning algorithm using the CNN model found that (1) The number of epochs can affect the accuracy of the CNN model, (2) 2. The application of architecture can affect the accuracy of the CNN model, (3) the application of the type of layer can affect the accuracy of the CNN model. Based on several problems in the research literature related to the identification of weaknesses and shortcomings of Deep Learning using the CNN model which refers to Table 1. State of the Art summary of literature review research for the last five years, it can be concluded that to increase the accuracy of the CNN model, it is necessary to increase the number of epochs, apply the right architecture according to the problems in the research conducted, and use the type of layer. The hypothesis of this article can be used as a reference for further research related to Deep Learning using the CNN model.

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Section: Summary Of Research Literature Reviewmentioning

confidence: 99%

Review of Deep Learning Using Convolutional Neural Network Model

Kurniawan,

Erlangga,

Tanjung

et al. 2024

Engineering Headway

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“…Biometrik merupakan sebuah metode otentikasi dan menjadi sistem yang paling efektif untuk melakukan pengenalan Personal, karena biometrik memiliki karakteristik yang unik, tidak dapat dibagikan, tidak bisa dilupakan, dan tidak bisa hilang ataupun dirampas [6]. Teknologi saat ini telah menggunakan berbagai bentuk sistem biometrik diantaranya yaitu Pengenal Wajah [7], Sidik Jari [8], Iris Mata [9], Garis Telapak Tangan [10], Pembuluh Darah Punggung Tangan [11], untuk melakukan otentikasi pengenalan Personal, seperti sistem keamanan sidik jari dan pengenalan wajah pada smartphone.…”

Section: Pendahuluanunclassified

Comparison of Gabor Filter Parameter Characteristics for Dorsal Hand Vein Authentication Using Artificial Neural Networks

Putra,

Yudono,

Sujjada

2023

SISFOKOM

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The importance of digital security in today's technological era requires various innovations in creating a reliable security system for humans. Biometrics is an authentication method and the most effective system for performing personal recognition because biometrics have unique characteristics. Dorsal hand vein become biometrics for the individual recognition process in this study using feature extraction of gabor filters and neural network backpropagation to classify recognition into five classes of human individuals, which are expected to be able to provide a higher accuracy value when compared to research on the introduction of dorsal hand vein. This classification process has several stages, namely input image, image pre-processing, segmentation, feature extraction, and image classification. The test results show that the percentage of success based on the five test scenarios has an average value of 75%. In this study, the results of the greatest test accuracy in the fourth scenario were 91%.

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