Skripsi

PENGENALAN KYOUIKU KANJI GRADE 1 DENGAN MENERAPKAN METODE MOBILENET V2 BERBASIS ANDROID

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Penilaian

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Character recognition has become a popular research topic in the field of pattern recognition and machine learning, including handwriting recognition, specifically kanji handwriting. This study performs handwriting recognition of kyouiku kanji grade 1, which is the kanji required to be learnt by grade 1 elementary school students in Japan. This research uses ETL-9B dataset from Electrotechnical Laboratory (now AIST), uses CNN MobileNet V2 deep learning method that has been customized for mobile devices, and uses Android application as the user interface implementation. Based on the study results, the highest accuracy model was obtained with an accuracy of 96,6875% and a size of 27.4MB for the alpha 1.0 hyperparameter. It can be concluded that the CNN MobileNet V2 deep learning method has performed quite well in the process of recognizing handwritten kyouiku kanji grade 1.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407004363	T151235	T1512352024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1512352024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xi, 61 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.370 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Kecerdasan Buatan
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

KA

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