Pengenalan Tulisan Tangan Angka menggunakan CNN dengan Arsitektur DenseNet-201 pada Dataset MNIST

Muhammad Fajril Fadillah Lubis(1), Susilawati Susilawati(2),


(1) Universitas Medan Area
(2) Universitas Medan Area
DOI: https://doi.org/10.34007/incoding.v5i1.826

Keywords


Deep Learning; Densenet-201; CNN; Digital Image Processing

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References


B. G. e. all, “Handwritten Digits Identification Using Mnist Database Via,” IOP Conference Series: Materials Science and Engineering, pp. 1-12, 2021.

K. e. a. S, “CNN Model for Image Classification on MNIST,” Journal of Scientific Research, pp. 374-384, 2020.

F. S. e. all, “DenseNet-201 and Xception Pre-Trained Deep Learning Models,” Electronics, pp. 1-23, 2023.

Y. W. e. all, “Improvement of MNIST Image Recognition Based,” IOP Conf. Series: Earth and Environmental Science, pp. 1-8, 2020.

H. H. e. all, “Driver Drowsiness Detection Based On the DenseNet 201 Model,” Turkish Journal of Computer and Mathematics Education, pp. 3682-3692, 2021.

C. A. T. Jaby, “Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201,” Borneo Journal of Resource Science and Technology, pp. 125-134, 2022.

M. B. e. all, “DenseNet Based Model for Plant Diseases Diagnosis,” European Journal of Electrical Engineering and Computer Science, pp. 1-9, 2022.

K. Adam, “A Selective Mitigation Technique of Soft Errors for DNN Models Used in Healthcare Application :DenseNet201 Case Study,” Digital Object Identifier, pp. 65803-65832, 2021.

P. S. dkk, “Identifikasi Penyakit Tanaman Padi Melalui Citra Daun Menggunakan DenseNet 201,” JOMLAI: Journal of Machine Learning and Artificial Intelligence, pp. 143-150, 2022.

R. e. all, “Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique,” PLOS ONE, pp. 1-23, 2024.

N. R. e. all, “Enhancing Image Recognition on MNIST Dataset Through VGG16 in CNN,” Journal of Propulsion Technology, pp. 1895-1907, 2024.

Agus Eko Minarno et all, “Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine,” INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, vol. 6, no. 2, pp. 404-410, 2022.

S. A. et.all, “Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN),” Sensors, pp. 1-18, 2020.

M. S. e. all, “MNIST handwritten digit recognition with different CNN architectures,” Journal of Applied Technology and Innovation, pp. 7-10, 2021.




DOI: https://doi.org/10.34007/incoding.v5i1.826

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