Penerapan transfer learning pada convolutional neural networks dalam deteksi covid-19.

Umri, Buyut Khoirul; Delica, Visq

doi:10.36802/jnanaloka.2021.v2-no2-9-17

Cited by 2 publications

(2 citation statements)

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“…Created in Japan, CNN was later improved upon by Yann LeCun throughout his research, and Alex Krizhevsky ultimately won the ImageNet Large Scale Visual Recognition Challenge by using CNN, demonstrating CNN's superiority to other methods for classifying objects in images. The idea for CNN is based on the biological nervous system and described as a convolution that unifies many layers with parts that operate in parallel [10].…”

Section: Introductionmentioning

confidence: 99%

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Ichsan

Armita

Minarno

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

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Section: Introductionmentioning

confidence: 99%

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Ichsan

Armita

Minarno

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

show abstract

“…, the optimal machine learning model was obtained in the 6th scenario, namely 89% with a validation accuracy of 81%, but the model size was quite large to deploy to the Raspberry Pi 4. This comparison was produced because of the transfer model training process[16][17]. The learning uses an existing CNN layer, namely SSDMobileNetV2, and the steps per epoch during training reach 40,000.…”

mentioning

confidence: 99%

Visual Impaired Assistance for Object and Distance Detection Using Convolutional Neural Networks

Parenreng,

Andi Baso Kaswar,

Ibnu Fikrie Syahputra

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Vision is a very valuable gift from God; Most aspects of human needs in the body are dominated by vision. Based on data from the World Health Organization (WHO) there are around 180 million people in the world experiencing visual impairment, while the prevalence of blindness in Indonesia reaches 3 million people (1.5% of Indonesia's population), so we designed a system in the form of a prototype that could detect objects around the user and convey data in the form of sound to the user. This research discusses the application of a machine learning model using the Convolutional Neural Network method to detect objects optimally. The objects that have been collected will be trained on machine learning and produce a model to be embedded in the system's main machine, namely the Raspberry PI 4B. The training of the machine learning model was carried out several times by changing the compositions of several layers until a model with optimal accuracy was obtained; however, the size of the resulting model was quite large, so the researchers carried out SSDMobileNetV2 transfer learning to obtain the optimal model. The optimal model was obtained with a model precision of 92% and a model size of 18 MB. Object detection tests carried out under 3 test conditions resulted in an average object detection accuracy of 84.3%, and distance detection tests carried out under 10 conditions resulted in an average distance detection error of 2.1 cm. The results show that the system was accurate and effective.

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Penerapan transfer learning pada convolutional neural networks dalam deteksi covid-19.

Cited by 2 publications

References 0 publications

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Visual Impaired Assistance for Object and Distance Detection Using Convolutional Neural Networks

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