Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network

Kurniadi, Felix Indra

doi:10.35957/jatisi.v8i1.568

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Harjoseputro, (2018) [14] classifying Javanese characters using the Convolutional Neural Network (CNN) method produces an overall accuracy rate of 85% using 1000 training images and 100 test images. Then the level of accuracy when viewed as a group of Javanese characters, it can be concluded that groups 1 and 3 with an accuracy rate of 92%, while the lowest level of accuracy when viewed per group of Javanese characters is group 2 with an accuracy rate of 72%.…”

Section: Peng Et Al (2020)mentioning

confidence: 99%

Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks

Widiharso

Handoko

Wibawa

et al. 2021

SITech

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Measuring the quality of river water used as industrial wastewater disposal is needed to maintain water quality from pollution. The chemical industry produces hazardous waste containing toxic materials and heavy metals. At specific concentrations, industrial waste can result in bacteriological contamination and excessive nutrient load (eutrophication). Using the Convolutional Neural Network (CNN), the method for measuring water quality processes remote sensing images taken via an RGB camera on an Unmanned Aerial Vehicle (UAV). The parameter measured is the change in the color of the river water image caused by the chemical reaction of the heavy metal content of industrial waste disposal. The test results of the Convolutional Neural Network (CNN) method in 2.01s/step obtained the value of training loss mode 17.86%, training accuracy 90.62%, validation loss 23.43%, validation accuracy 83.33%.

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Section: Peng Et Al (2020)mentioning

confidence: 99%

Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks

Widiharso

Handoko

Wibawa

et al. 2021

SITech

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“…Other research using Convolutional Neural Networks for Javanese script classification obtained accuracy results of 85%. The dataset used is 20 classes of Javanese script data, each of which is contained in each folder containing 108 images [11].…”

Section: Introductionmentioning

confidence: 99%

Classification of Javanese Nglegena Script Using Complexvalued Neural Network

Rahmawati,

Muhaimin,

Prasetya

2024

jiko

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Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.

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“…The process of distinguishing between poisonous and non-poisonous mushrooms is critical for preserving ecosystems, human health, and safety [2]. Deep learning is the method approach that is most frequently implemented [1], [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

Lia Farokhah,

Suastika Yulia Riska

2024

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

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One food item that is easy to find in nature is the mushroom. In terms of form and characteristics, mushrooms are similar. Arranging mushrooms into groups so that poisonous and non-poisonous ones can be separated is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The data set used is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16, VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. When the confusion matrix is used for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower computation architecture and cuts down on unnecessary preprocessing. This means that many people can use smartphones with more realistic data conditions to make it work.

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Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network

Cited by 4 publications

References 4 publications

Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks

Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks

Classification of Javanese Nglegena Script Using Complexvalued Neural Network

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

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