Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal SMATIKA Smatika Jurnal : STIKI Informatika Jurnal
Uce Indahyanti
Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Computer Science & IT Education

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

Found 2 Documents
Search

 1 
Implementasi Convolutional Neural Network (CNN) Untuk Pengenalan Tulisan Tangan Aksara Bima Ahmad Angga Handoko; Mochamad Alfan Rosid; Uce Indahyanti
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1196

Abstract

Indonesia with its rich cultural diversity, certainly results in the existence of various languages in it. In the dialect of a region, there are letter symbols that represent the expression of the region's unique language. One example of a language that shows unique characteristics in its writing system is Bima, known as the Bima script. Bima script, or often referred to as Mbojo script, is a writing system traditionally used in the Bima region, located in West Nusa Tenggara Province. Bima script is still not widespread among the public, so it is important to preserve it as part of the cultural heritage of the Mbojo tribe. This research aims to train a computer to recognize Bima characters. The Convolutional Neural Network (CNN) method is used in this research to recognize Bima script handwriting. The dataset used consists of 2640 images of Bima script handwriting with 22 classes. The results showed a reliable performance of the CNN model, with an accuracy of 97,34%, precision 97,56%, recall 97,34%, and f1-score 97,31% on the test data.
Implementasi Convolutional Neural Network (CNN) Untuk Pengenalan Tulisan Tangan Aksara Bima Ahmad Angga Handoko; Mochamad Alfan Rosid; Uce Indahyanti
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1196

Abstract

Indonesia with its rich cultural diversity, certainly results in the existence of various languages in it. In the dialect of a region, there are letter symbols that represent the expression of the region's unique language. One example of a language that shows unique characteristics in its writing system is Bima, known as the Bima script. Bima script, or often referred to as Mbojo script, is a writing system traditionally used in the Bima region, located in West Nusa Tenggara Province. Bima script is still not widespread among the public, so it is important to preserve it as part of the cultural heritage of the Mbojo tribe. This research aims to train a computer to recognize Bima characters. The Convolutional Neural Network (CNN) method is used in this research to recognize Bima script handwriting. The dataset used consists of 2640 images of Bima script handwriting with 22 classes. The results showed a reliable performance of the CNN model, with an accuracy of 97,34%, precision 97,56%, recall 97,34%, and f1-score 97,31% on the test data.
Co-Authors Ahmad Angga Handoko Mochamad Alfan Rosid