PENGENALAN HURUF KATAKANA MENGGUNAKAN METODE ELMAN RECURRENT NEURAL NETWORK

ARDIANAWATI, YULISKA and Samsuryadi, Samsuryadi and Miraswan, Kanda Januar (2020) PENGENALAN HURUF KATAKANA MENGGUNAKAN METODE ELMAN RECURRENT NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Katakana Japanese characters has its own difficulty level in writing for non-Japanese people. Katakana is very different from the commonly used letters of the alphabet, katakana is also difficult to remember by non-Japanese nation because of its unique shape. Therefore we need software that can help recognize katakana. Data taken as many as 20 people at each of the 46 letters and is written as much as 5 times of students Japanese language courses at Language Institute Magenta. The software is built using Elman Recurrent Neural Network as recognition methods and Haar Wavelet method as feature extraction method. The results of the study of handwriting recognition katakana produce an accuracy of 74.35%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Teknik Informatika, Jaringan Syaraf Tiruan
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 4027 not found.
Date Deposited:	21 Jan 2020 04:05
Last Modified:	21 Jan 2020 08:31
URI:	http://repository.unsri.ac.id/id/eprint/24514

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