Daftar Isi: PENGENALAN HURUF HIJAIYAH TULISAN TANGAN DENGAN MENGGUNAKAN EKSTRAKSI CIRI GEOMETRIC MOMENT INVARIANT DAN SELF ORGANIZING MAPS

Main Authors:	ARMILIA, SONIA, Efendi, Rusdi
Format:	Thesis NonPeerReviewed Book
Bahasa:	ind
Terbitan:	, 2018
Subjects:	T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Online Access:	http://repository.unsri.ac.id/8099/1/RAMA_55201_09111402032_compressed.pdf http://repository.unsri.ac.id/8099/2/RAMA_55201_09111402032_8826630017_01_front_ref.pdf http://repository.unsri.ac.id/8099/3/RAMA_55201_09111402032_8826630017_02.pdf http://repository.unsri.ac.id/8099/4/RAMA_55201_09111402032_8826630017_03.pdf http://repository.unsri.ac.id/8099/5/RAMA_55201_09111402032_8826630017_04.pdf http://repository.unsri.ac.id/8099/6/RAMA_55201_09111402032_8826630017_05.pdf http://repository.unsri.ac.id/8099/7/RAMA_55201_09111402032_8826630017_06_ref.pdf http://repository.unsri.ac.id/8099/

Daftar Isi:

Handwriting hijaiyah recognition has a high degree of difficulty. Handwriting recognition problems are the size and shape of handwriting that are not fixed. This study aims to develop Self Organizing Maps (SOM) Algorithm to recognize handwritten hijaiyah. Also in this study using extraction feature Geometric Moment Invariant (GMI). The data used in the study using primary data in the form of handwritten hijaiyah image. The result of handwriting hijaiyah recognition using GMI and SOM has an accuracy of 95%.Because in the extraction of GMI characteristics unchanged on rotational treatment, this study made the introduction of handwriting hijaiyah data that rotated with an accuracy of 92%.