Pengenalan huruf dengan algoritma genetik = Genetic algorithm for alphabet recognition

Effendy, Effendy (2007) Pengenalan huruf dengan algoritma genetik = Genetic algorithm for alphabet recognition. Bachelor thesis, Universitas Pelita Harapan.

[img]
Preview
Text (Cover)
Cover.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (20kB) | Preview
[img]
Preview
Text (Abstract)
Abstract.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (9kB) | Preview
[img]
Preview
Text (ToC)
ToC.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (29kB) | Preview
[img]
Preview
Text (Chapter 1)
Chapter 1.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (24kB) | Preview
[img] Text (Chapter 2)
Chapter 2.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (269kB)
[img] Text (Chapter 3)
Chapter 3.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (122kB)
[img] Text (Chapter 4)
Chapter 4.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (431kB)
[img] Text (Chapter 5)
Chapter 5.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (12kB)
[img]
Preview
Text (Bibliography)
Bibliography.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (74kB) | Preview
[img] Text (Appendices)
Appendices.pdf
Restricted to Repository staff only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (1MB)

Abstract

As a searching algorithm, Genetic Algorithm imitates the principles of natural selection and natural genetics. In nature, a good chromosome will survive so the chromosome in the next generation will become better. In one cycle of generation, there are some methods such as selection, recombination and mutation to get the best chromosome in a generation. In Alphabet recognition, genetic algorithm software is developed for searching the best assignment between coordinate representation of an observed alphabet and coordinate representation of a model alphabet. An alphabet is recognized when the error value of the total distance between coordinate representation of an observed alphabet and coordinate representation of a model alphabet approaches zero. The sum of distance can be calculated by searching the optimum transformation parameter that can minimize the error of the coordinate coupling. Experimental results show that genetic algorithm could find the best assignment even though the observed alphabet has undergone a similarity transformation such as rotation, translation, and scaling. In the experiment of finding the optimum value for genetic variable, defining the crossover probability value in the first place will get a better result than mutation probability. The experimental results show that the percentage of recognition success reached 83,6%.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Effendy, EffendyNIM08220030049UNSPECIFIED
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorAribowo, ArnoldNIDN0304057602arnold.aribowo@uph.edu
Thesis advisorSitorus, Budi BerlintonUNSPECIFIEDUNSPECIFIED
Additional Information: T 45752
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > School of Information Science and Technology > Informatics
Current > Faculty/School - UPH Karawaci > School of Information Science and Technology > Informatics
Depositing User: Stefanus Tanjung
Date Deposited: 06 May 2024 08:29
Last Modified: 06 May 2024 08:30
URI: http://repository.uph.edu/id/eprint/62958

Actions (login required)

View Item View Item