ABSTRAK
Pengenalan tulisan tangan telah lama diidentifikasikan sebagai permasalahan yang sulit dipecahkan oleh komputer karena karakteristik tulisan tangan setiap orang berbeda-beda. Pada penelitian ini, jaringan saraf tiruan backpropagation digunakan dalam mengenali tulisan tangan aksara Batak Toba melalui beberapa tahap pra proses meliputi binarization, memorization dan thinning, tahap selanjutnya adalah ekstraksi fitur menggunakan kombinasi metode zoning dan diagonal based feature extraction. Kedua metode ini sama-sama membagi data sampel menjadi NxM zona dan menghitung nilai fitur dari setiap zona tersebut. Data sampel dibagi menjadi 6x9 zona, yaitu 54 zona dengan ukuran masing-masing zona adalah 10x10 piksel. Metode
zoning menghitung jumlah piksel hitam setiap zona dan melakukan perbandingan
terhadap zona yang memiliki jumlah piksel hitam paling banyak. Metode diagonal
based feature extraction menghitung nilai rata-rata histogram diagonal setiap zona.
Selain itu juga dihitung rata-rata nilai zona setiap baris dan kolom. Berdasarkan ekstraksi fitur tersebut didapatkan 123 nilai fitur, yaitu 54 dari metode zoning dan 69 dari metode diagonal based feature extraction. Nilai fitur tersebut dijadikan masukan untuk proses klasifikasi menggunakan jaringan backpropagation. Pada penelitian ini digunakan 114 data untuk pelatihan dan 76 data untuk pengujian. Dari pengujian yang dilakukan diperoleh tingkat pengenalan menggunakan jaringan saraf tiruan
backpropagation dengan kombinasi dua metode fitur ekstraksi adalah 87,19%.
Kata kunci : jaringan saraf tiruan, backpropagation, diagonal based feature
extraction, zoning, tulisan tangan, aksara Batak Toba.
HAND-WRITING RECOGNITION IN BATAK TOBA SCRIPT USING BACKPROPAGATION NEURAL NETWORK
ABSTRACT
Hand-writing recognition had been identified as a complicated problem to solve by computation that is because every single person has different characteristic of writing. In this research, backpropagation of neural network been used to recognize in hand-writing of Batak Toba script by certain phases of preprocessing including binarization, memorization and thinning, feature extraction to the next phase using the combination of zoning method and diagonal based feature extraction. Both of these methods worked together to devide sample data into NxM zone and compute a feature value for each zone. Sample data is divided into 6x9 zone which is amount to 54 zones with each zone has 10x10 pixel size. Zoning method computed the amount for black pixel in each zone and collated zones that have the most quantity of black pixel. On the other hand, the diagonal based feature extraction method computed an average value for diagonal histogram in each zone and also computed average value for each rows and colomns. Based on the extraction feature that had been done, we gained 123 feature values, that is 54 for the zoning method and 69 for the diagonal based feature extraction method. Those feature values became input for classification process using backpropagation network. On this research, had 114 training data and 76 testing data that been used. Based on the research that been done, we result recognition level using backpropagation neural network with a combination of two feature extractions is 87.19%.
Keywords: Neural network, Backpropagation, diagonal based feature extraction,
zoning, hand-writing, Batak Toba Script.
