PENGENALAN KARAKTER TULISAN TANGAN HANGUL SATU SUKU KATA MENGGUNAKAN CNN DENGAN ARSITEKTUR RESNET DAN ALGORITMA SVM

Rosalinda, Rena Rama (2025) PENGENALAN KARAKTER TULISAN TANGAN HANGUL SATU SUKU KATA MENGGUNAKAN CNN DENGAN ARSITEKTUR RESNET DAN ALGORITMA SVM. Undergraduate thesis, UPN Veteran Jatim.

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Abstract

Foreign language skills are one of the gateways to opening up opportunities for better education and employment. The use of technology can help with this, especially handwriting recognition technology. However, the use of limited datasets is often a problem. This study uses a Convolutional Neural Network (CNN) model with a Residual Network (ResNet) architecture and a Support Vector Machine (SVM). ResNet, as a feature extraction method for the data, is capable of capturing data patterns without losing much of the original data information. Meanwhile, the SVM algorithm, as a data classifier, is capable of working well with limited data. This research uses hyperparameters of linear kernel, polynomial kernel, Radial Basis Function (RBF) kernel, and Sigmoid kernel. Additionally, the hyperparameters C and Gamma values were also used. The research results indicate that the best model accuracy was obtained from the model trained with a linear kernel and a C value of 0.1, with an accuracy of 81.72% and an accuracy on the test data of 87.50%. Keywords: Convolutional Neural Network (CNN), Handwritten Recognition, Residual Network (ResNet), Support Vector Machine (SVM)

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Rahmat, Basuki	NIDN5972549	basukirahmat.if@upnjatim.ac.id
Thesis advisor	Mandyartha, Eka Prakarsa	NIDN0725058805	eka_prakarsa.fik@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Rena Rama Rosalinda

Date Deposited:

15 Sep 2025 06:23

Last Modified:

15 Sep 2025 06:23

URI:

https://repository.upnjatim.ac.id/id/eprint/42436