Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan
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Abstract
Jawi is one variant of Arabic script consists of 35 characters. Some of Jawi characters have the same main shape, but different number of dots in different location. Thus, recognition process of Jawi characters can be done by performing a classification based on the main shape. In recognition process, feature extraction plays an important role. In this research, Freeman Chain Code (FCC) was used as feature extraction and Support Vector Machine (SVM) as classifier. Then we apply the decision rules to classifySVMresult into Jawi characters. FCC is used to represent the boundary of Jawi characters into a chain code. Then the chain code is used bySVMto classify the characters into 19 groups. Feature of location and the number of dots are used by decision rules to classify the groups into Jawi characters. The Jawi characters are handwritten and generated by 10 writers from different backgrounds and ages. The recognition rate of this research was 80.00%.
Keywords : Jawi script, handwriting, FCC, SVM, decision rules.
Abstrak—Aksara Jawi merupakan salah satu varian dari aksara Arab yang terdiri dari 35 aksara. Dari 35 aksara Jawi tersebut terdapat beberapa aksara dengan bentuk bagian utama yang sama namun memiliki letak dan jumlah titik yang berbeda. Karena perbedaan tersebut maka proses pengenalan aksara Jawi dapat dilakukan dengan melakukan klasifikasi berdasarkan perbedaan bentuk bagian utama. Pada penelitian ini Freeman Chain Code (FCC) digunakan sebagai ekstraksi fitur dan Support Vector Machine (SVM). FCC digunakan untuk merepresentasikan garis batas (boundary) aksara Jawi kedalam kode rantai. Kode rantai tersebut diklasifikasi dengan menggunakan SVM kedalam 19 kelompok. Fitur letak titik dan jumlah titik digunakan sebagai aturan pengambilan keputusan terhadap 19 kelompok hasil klasifikasi SVM kedalam aksara Jawi. Aksara Jawi yang digunakan merupakan tulisan tangan dari 10 orang penulis dari berbagai latar belakang dan umur. Tingkat keberhasilan klasifikasi penelitian ini mencapai 80,00%.
Kata Kunci : aksara Jawi, tulisan tangan, FCC, SVM, aturan pengambilan keputusan
References
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Safrizal ., Universitas Syiah Kuala
Safrizal, dilahirkan di Aceh Utara. Pendidikan SD sampai SMA dilalui di Aceh Utara. Lulus SMA melanjutkan kuliah dan memperoleh gelar sarjana di jurusan teknik elektro Universitas Syiah Kuala pada tahun 2006. Saat ini masih menempuh pendidikan magister teknik elektro di Universitas Syiah Kuala.
Fitri Arnia, Universitas Syiah Kuala
Fitri Arnia memperoleh gelar Sarjana Teknik dari Universitas Sumatera Utara (USU), Medan pada tahun 1997. Pendidikan tingkat master dan doktoral diselesaikannya pada University of New South Wales (UNSW), Sydney, Australia dan Tokyo Metropolitan University, Jepang berturut-turut pada tahun 2004 dan 2008. Beliau bekerja sebagai staf pengajar pada Jurusan Teknik Elektro, Fakultas Teknik, Universitas Syiah Kuala sejak tahun 1999. Beliau juga anggota IEEE dan IAENG. Bidang penelitiannya meliputi pengolahan sinyal, citra dan multimedia digital.