Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Tjut Awaliyah Zuraiyah, A Qur 'ania, C R Pitoyo

Judul : ptimization of Feature Extraction Using Combined Image Centroid Zone and Zone Centroid Zone Method

Abstrak :
Computer vision is one of computer science part that imitates how the human visual works so that the computer can recognize the object. The process of object recognition by computer requires a certain process, one of the example is the handwriting recognition using zoning as a method for feature extraction and support vector machine as a method for classification. The method used was Image Centroid Zone (ICZ), Zone Centroid Zone (ZCZ), and the combined ICZ & ZCZ with 8 zone. Support Vector Machine used was Radial Basis Function (RBF) kernel. The Stages of research conducted including data collection, analysis, preprocessing, feature extraction, classification and testing. The highest accuracy value for the uppercase obtained by using combined zoning ICZ & ZCZ of 88.46%, while 76.92% for the lowercase. The highest accuracy value with slope of 10o and 20o for uppercase obtained using zoning ICZ of 71.15%, while the highest accuracy for lowercase with slope of 10o obtained using zoning ICZ of 76.92% and 59.61% with slope of 20o .

Tahun : 2016 Media Publikasi : Jurnal Nasional Blm Akreditasi

Kategori : Prosiding No/Vol/Tahun : - / - / 2016

ISSN/ISBN : -

PTN/S : Universitas Pakuan Bogor Program Studi : ILMU KOMPUTER

Bibliography :
[1] Arridho et al. 2013. Analisis Pen Pressure Tulisan Tangan Untuk Mengidentifikasi Kepribadian Seseorang Menggunakan Support Vector Machine (SVM). Diponegoro University. Semarang.

[2] Rajashekararadhya SV & Ranjan Dr PV. 2008. Eficient Zone Based Feature Extraction Algorithm For Handwritten Numeral Recognition Of Four Popular South Indian Scripts. Journal of Theoretical and Applied Information Technology.

[3] Syam RM. 2013. Pengenalan Aksara Jawa Tulisan Tangan dengan Menggunakan Ektraksi Fitur Zoning dan Klasifikasi K-Nearest Neighbour. Skripsi. Bogor Agricultural University. Bogor.

[4] Mulia I. 2012. Pengenalan Aksara Sunda Menggunakan Ekstraksi Ciri Zoning dan Klasifikasi Support Vector Machine. Skripsi. Bogor Agricultural University. Bogor.

[5] Nugroho et al. 2003. Suport Vector Machine Teori dan Aplikasinya dalam Bioinformatika. IlmuKomputer.Com.

URL : https://www.researchgate.net/publication/316011674_Optimization_of_Feature_Extraction_Using_Combined

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Detail Karya Ilmiah Dosen

Tjut Awaliyah Zuraiyah, A Qur 'ania, C R Pitoyo

Judul	:	ptimization of Feature Extraction Using Combined Image Centroid Zone and Zone Centroid Zone Method
Abstrak	:	Computer vision is one of computer science part that imitates how the human visual works so that the computer can recognize the object. The process of object recognition by computer requires a certain process, one of the example is the handwriting recognition using zoning as a method for feature extraction and support vector machine as a method for classification. The method used was Image Centroid Zone (ICZ), Zone Centroid Zone (ZCZ), and the combined ICZ & ZCZ with 8 zone. Support Vector Machine used was Radial Basis Function (RBF) kernel. The Stages of research conducted including data collection, analysis, preprocessing, feature extraction, classification and testing. The highest accuracy value for the uppercase obtained by using combined zoning ICZ & ZCZ of 88.46%, while 76.92% for the lowercase. The highest accuracy value with slope of 10o and 20o for uppercase obtained using zoning ICZ of 71.15%, while the highest accuracy for lowercase with slope of 10o obtained using zoning ICZ of 76.92% and 59.61% with slope of 20o .
Tahun	:	2016	Media Publikasi	:	Jurnal Nasional Blm Akreditasi
Kategori	:	Prosiding	No/Vol/Tahun	:	- / - / 2016
ISSN/ISBN	:	-
PTN/S	:	Universitas Pakuan Bogor	Program Studi	:	ILMU KOMPUTER
Bibliography	:	[1] Arridho et al. 2013. Analisis Pen Pressure Tulisan Tangan Untuk Mengidentifikasi Kepribadian Seseorang Menggunakan Support Vector Machine (SVM). Diponegoro University. Semarang. [2] Rajashekararadhya SV & Ranjan Dr PV. 2008. Eficient Zone Based Feature Extraction Algorithm For Handwritten Numeral Recognition Of Four Popular South Indian Scripts. Journal of Theoretical and Applied Information Technology. [3] Syam RM. 2013. Pengenalan Aksara Jawa Tulisan Tangan dengan Menggunakan Ektraksi Fitur Zoning dan Klasifikasi K-Nearest Neighbour. Skripsi. Bogor Agricultural University. Bogor. [4] Mulia I. 2012. Pengenalan Aksara Sunda Menggunakan Ekstraksi Ciri Zoning dan Klasifikasi Support Vector Machine. Skripsi. Bogor Agricultural University. Bogor. [5] Nugroho et al. 2003. Suport Vector Machine Teori dan Aplikasinya dalam Bioinformatika. IlmuKomputer.Com.
URL	:	https://www.researchgate.net/publication/316011674_Optimization_of_Feature_Extraction_Using_Combined