Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal International Journal of New Media Technology
Al Fitra Yudha, Vasyilla Kautsar
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Approach Convolutional Neural Network LeNet-5 for Interactive Learning of Korean Syllables (Hangul) Al Fitra Yudha, Vasyilla Kautsar; Kurniasari, Arvita Agus; Arifianto, Aji Seto; Afriansyah, Faisal Lutfi
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3705

Abstract

The increasing popularity of South Korean culture among Indonesian society has led to a growing interest in gaining a deeper understanding of the country, including a desire to master the Korean language. However, learning the Korean alphabet (hangul) often presents challenges due to its characters being unfamiliar to the Indonesian people. Therefore, engaging and interactive learning media are needed to assist in the learning process. Within this endeavor, a learning website called Learn Hangul was developed, focusing on two main features: learning hangul characters and their arrangement, as well as practicing writing syllables using Korean letters. This website was developed using the Convolutional Neural Network (CNN) LeNet-5 to facilitate learning, with black box testing results indicating good functionality. Model performance evaluation yielded satisfactory values, with model accuracy at 89.2%, precision at 89.7%, recall at 88.8%, and an F1-score of 89.2%. Direct testing with users also showed a high success rate, with 80% of respondents experiencing an increase in their knowledge of Korean characters (Hangul) after trying to learn them on the Learn Hangul website. Thus, the Learn Hangul website serves as a useful learning tool for those interested in studying the Korean alphabet (hangul).
Approach Convolutional Neural Network LeNet-5 for Interactive Learning of Korean Syllables (Hangul) Al Fitra Yudha, Vasyilla Kautsar; Kurniasari, Arvita Agus; Arifianto, Aji Seto; Afriansyah, Faisal Lutfi
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3705

Abstract

The increasing popularity of South Korean culture among Indonesian society has led to a growing interest in gaining a deeper understanding of the country, including a desire to master the Korean language. However, learning the Korean alphabet (hangul) often presents challenges due to its characters being unfamiliar to the Indonesian people. Therefore, engaging and interactive learning media are needed to assist in the learning process. Within this endeavor, a learning website called Learn Hangul was developed, focusing on two main features: learning hangul characters and their arrangement, as well as practicing writing syllables using Korean letters. This website was developed using the Convolutional Neural Network (CNN) LeNet-5 to facilitate learning, with black box testing results indicating good functionality. Model performance evaluation yielded satisfactory values, with model accuracy at 89.2%, precision at 89.7%, recall at 88.8%, and an F1-score of 89.2%. Direct testing with users also showed a high success rate, with 80% of respondents experiencing an increase in their knowledge of Korean characters (Hangul) after trying to learn them on the Learn Hangul website. Thus, the Learn Hangul website serves as a useful learning tool for those interested in studying the Korean alphabet (hangul).
Co-Authors Afriansyah, Faisal Lutfi Aji Seto Arifianto Arvita Agus Kurniasari Kurniasari, Arvita Agus