Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn)

Fadlia, Nur; Kosasih, Rifki

doi:10.35760/tr.2019.v24i3.2397

Cited by 18 publications

(18 citation statements)

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“…27,28 The accuracy of CNN's algorithm performance is an indicator to show the system's ability to classify the input data proceed in the system. 29 From Table 2, it shows that only two experiments show the accuracy and both are above 0.800 which means they have a great ability in classifying input data which will be proceed by the system.…”

Section: Numbers Of Samples Formentioning

confidence: 98%

Artificial intelligence based on Convolutional Neural Network for detecting dental caries on bitewing and periapical radiographs

Roosanty

Widyaningrum

Diba

2022

JRDI

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Objectives: This narrative review is written to describe the accuracy of caries detection and find out the clinical implications and future prospects of using Convolutional Neural Network (CNN) to determine radio-diagnosis of dental caries in bitewing and periapical radiographs. Review: The databases used for literature searching in this narrative review were PubMed, Google Scholar, and Science Direct. The inclusion criteria were original article, case report, and textbook written in English and Bahasa Indonesia, published within 2011-2021. The exclusion criteria were articles that the full text could not be accessed, research article that did not provide the methods used, and duplication articles. In this narrative review, a total of 33 literatures consisting of 30 articles and three textbooks reviewed, including four original articles on CNN for caries detection. Conclusion: Results of the review reveal that GoogLeNet produces the best detection compared to Fully Convolutional Network (FCN) and U-Net for caries detection in bitewing and periapical radiographs. Nonetheless, the positive predictive value (PPV), recall, negative predictive value (NPV), specificity, F1-score, and accuracy values in these architectures indicate good performance. The differences of each CNN’s performances to detect caries are determined by the number of trained datasets, the architecture’s layers, and the complexity of the CNN architectures. The conclusion of this review is CNN can be used as an alternative to detect caries, increasing the diagnostic accuracy and time efficiency as well as preventing errors due to dentist fatigue. Yet the CNN is not able to substitute the expertise of a radiologist. Therefore, it is need to be revalidated by the radiologist to avoid diagnostic errors.

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Section: Numbers Of Samples Formentioning

confidence: 98%

Artificial intelligence based on Convolutional Neural Network for detecting dental caries on bitewing and periapical radiographs

Roosanty

Widyaningrum

Diba

2022

JRDI

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“…The convolutional layer is represented in a matrix that has the same dimensions as the input layer. For example, if the input is 2D, then the Conv layer is also 2D [12]. The layer size is smaller than the input, which is commonly used as 3x3 for 2D.…”

Section: Input Layersmentioning

confidence: 99%

Improvement Of Image Quality Using Convolutional Neural Networks Method

Nugroho

Permadi

Faturrahim

2022

SJI

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Abstract. Purpose: This desire for high resolution stems from two main application areas, namely improving pictorial information for human interpretation and assisting automatic machine perception in representing images or videos. Image resolution describes the detail contained in an image, the higher the resolution, the more detail there is. The resolution of a digital image can be classified into various types, namely pixel resolution, spatial resolution, temporal resolution, and radiometric resolution. In this context, we are interested in spatial resolution.Methods: Elements of a digital image consist of a collection of small images called pixels. Spatial resolution refers to the pixel density of an image and is measured in pixels per unit area. A quality digital image is determined by the size of the resolution it has. A low resolution or low-resolution is a drawback of a digital image because the information contained in the image means little compared to a high-resolution image.Result: Therefore, in this study, a digital image processing program was created in the form of Image Super-Resolution with the Convolutional Neural Network method to utilize low-resolution images to produce high-resolution images. With a fairly short training process, namely 6050 datasets with 100 CNN epochs, the average PSNR image is 5% higher.Novelty: Image quality can be improved by changing the parameters in the CNN method so that image quality can be improved.

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“…Model CNN merupakan suatu langkah dalam menyusun proses suatu model yang digunakan untuk mengolah data agar dapat mengenali objek yang digunakan. Model CNN ini meliputi jumlah layer yang digunakan, menentukan filter, penentuan ukuran kernel, penentuan fungsi aktivasi dan ukuran pool [5]. CNN merupakan salah satu jenis jaringan syaraf tiruan yang dapat digunakan untuk pengenalan dan pengolahan citra [6].…”

Section: Pendahuluanunclassified

Implementasi Model CNN Dan Tensorflow Dalam Pendeteksian Jenis Daging Hewan Ternak

Mellinia

2022

J-TIT

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Abstrak—Daging banyak dikonsumsi oleh masyarakat Indonesia dan banyak dijual di supermarket maupun pasar tradisional. Namun demikian , ada sebagian masyarakat yang kurang memiliki pengetahuan dalam memilih daging yang layak konsumsi dengan tepat. Penelitian ini menggunakan model Convolutional Neural Network (CNN) dan tensorflow untuk membangun sistem yang dapat mendeksi jenis daging yang layak konsumsi. Berdasarkan data yang didapat oleh peneliti data tersebut digunakan untuk pengolahan dataset yang nantinya akan dikelompokkan. Dataset tersebut terdapat 7 kategori dengan pengklasifikasian menggunakan model CNN. Model CNN cocok diterapkan karena model ini dapat memproses input gambar, yang menghasilkan sistem deteksi jenis daging yang layak dikonsumsi dengan akurasi 80,95%. Dengan sistem yang compatible dengan perangkat mobile, dengan CNN dapat memudahkan masyarakat memilih daging dengan tepat.

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Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn)

Cited by 18 publications

References 0 publications

Artificial intelligence based on Convolutional Neural Network for detecting dental caries on bitewing and periapical radiographs

Artificial intelligence based on Convolutional Neural Network for detecting dental caries on bitewing and periapical radiographs

Improvement Of Image Quality Using Convolutional Neural Networks Method

Implementasi Model CNN Dan Tensorflow Dalam Pendeteksian Jenis Daging Hewan Ternak

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