PENGENALAN CITRA TULISAN TANGAN MENGGUNAKAN METODE STRUCTURE FEATURE EXTRACTION DAN SELF ORGANIZING MAPS

CHARLES, CHARLES and Sazaki, Yoppy and Miraswan, Kanda Januar (2019) PENGENALAN CITRA TULISAN TANGAN MENGGUNAKAN METODE STRUCTURE FEATURE EXTRACTION DAN SELF ORGANIZING MAPS. Undergraduate thesis, Sriwijaya University.

	Text RAMA_55201_09021381419073.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_55201_09021381419073_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (13MB) | Request a copy
Preview	Text RAMA_55201_09021381419073_0006067406_0009019002_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB) | Preview
	Text RAMA_55201_09021381419073_0006067406_0009019002_02.pdf - Accepted Version Restricted to Repository staff only Download (389kB) | Request a copy
	Text RAMA_55201_09021381419073_0006067406_0009019002_03.pdf Restricted to Repository staff only Download (244kB) | Request a copy
	Text RAMA_55201_09021381419073_0006067406_0009019002_04.pdf Restricted to Repository staff only Download (2MB) | Request a copy
	Text RAMA_55201_09021381419073_0006067406_0009019002_05.pdf Restricted to Repository staff only Download (203kB) | Request a copy
	Text RAMA_55201_09021381419073_0006067406_0009019002_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (39kB) | Request a copy
	Text RAMA_55201_09021381419073_0006067406_0009019002_07_lamp.pdf Restricted to Repository staff only Download (34MB) | Request a copy

Abstract

The handwriting of each individual has unique characteristics, in Chinese writing it has the name scratches. This stroke can be used as one of the traits in an individual handwriting. This study will focus on handwriting recognition using the Self Organizing Maps method and Structure Feature Extraction, where handwriting that will be recognized is a two-variable linear equation. This equation will be broken down into per-character and will be recognized by using the Self Organizing Maps method and Structure Feature Extraction. The highest percentage of recognition accuracy of all six scenarios is 62.99%. The purpose of this study is to determine the level of accuracy obtained from the use of the Structure Feature Extraction and Self Organizing Maps methods, and the component influence of the Self Organizing Maps method on the level of accuracy, and this research can be used to recognize handwriting with other models. Tulisan tangan setiap individu memiliki ciri yang unik, dalam penulisan Bahasa China memiliki yang namanya goresan. Goresan ini dapat dijadikan sebagai salah satu ciri yang ada di dalam tulisan tangan individu. Penelitian ini akan berfokus pada pengenalan tulisan tangan dengan menggunakan metode Structure Feature Extraction dan Self Organizing Maps, dimana tulisan tangan yang akan dikenali berupa persamaan linier dua variabel. Persamaan ini akan dipecah menjadi per- karakter dan akan dikenali dengan menggunakan metode Structure Feature Extraction dan Self Organizing Maps. Nilai persentase akurasi pengenalan tertinggi dari semua enam skenario yang ada adalah sebesar 62,99%. Tujuan dari penelitian ini adalah untuk mengetahui tingkat akurasi yang diperoleh dari penggunaan metode Structure Feature Extraction dan Self Organizing Maps, dan pengaruh komponen dari metode Self Organizing Maps terhadap tingkat akurasi, serta penelitian ini dapat digunakan untuk mengenali tulisan tangan dengan model lain.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	tulisan tangan, persamaan dua variabel, goresan, tingkat akurasai, komponen self organizing maps
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T61-173 Technical education. Technical schools
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 4303 not found.
Date Deposited:	14 Jan 2020 05:50
Last Modified:	14 Jan 2020 05:50
URI:	http://repository.unsri.ac.id/id/eprint/24032

Actions (login required)

View Item