Classification of Calligraphy Writing Types Using Convolutional Neural Network Method (CNN)
Klasifikasi Jenis Tulisan Kaligrafi Menggunakan Metode Convolutional Neural Network (CNN)
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(1) Oddy Virgantara Putra Universitas Darussalam Gontor
Indonesia
(2) Aziz Musthafa Universitas Darussalam Gontor
Indonesia
(3) Muhammad Nur Universitas Darussalam Gontor
Indonesia
(4) * Muhamad Rido
Universitas Darussalam Gontor
Indonesia
(*) Corresponding Author
Abstract
Calligraphy is the art of beautiful Arabic writing in which a series of letters are formed in appropriate proportions, maintaining distance and accuracy containing verses from the Qur'an or Hadith. There is a challenge to recognize the type of calligraphy using machine learning. This study aims to classify the types of calligraphy writing for ordinary people who do not understand the differences between each type of calligraphy writing. This study builds a model using the Convolutional Neural Network (CNN) method. The image used will go through a noise cleaning, resizing, and cropping process. This method is to carry out the process of classifying the type of calligraphy using a dataset consisting of 230 of 2 different types of calligraphy, namely the Naskhi and Riq'ah types. 80% is used as training data and 20% for test data. In the modeling process there are two convolutional layers and two MaxPooling layers followed by a Fully connected layer. The CNN modeling results used to test the built data have an average percentage result of 89% accuracy from the training data used. For further research, it can be developed with other types of calligraphy.
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References
H. Makmur and Y. Abdullah, “Manifestasi khat naskhi sebagai tulisan asas Al-Quran: Kajian terhadap jenis khat naskhi sebagai tulisan asas Al- Quran mushaf uthmani,” no. January, 2011, [Online]. Available: https://www.researchgate.net/publication/282913527%0AMANIFESTASI.
M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” J. Algoritm., vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434. DOI: https://doi.org/10.35957/algoritme.v1i1.434
D. Li et al., “A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network,” Sensors (Switzerland), vol. 20, no. 3, 2020, doi: 10.3390/s20030578. DOI: https://doi.org/10.3390/s20030578
N. Altwaijry and I. Al-Turaiki, “Arabic handwriting recognition system using convolutional neural network,” Neural Comput. Appl., vol. 33, no. 7, pp. 2249–2261, 2021, doi: 10.1007/s00521-020-05070-8. DOI: https://doi.org/10.1007/s00521-020-05070-8
E. N. Arrofiqoh and Harintaka, “IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN PADA CITRA RESOLUSI TINGGI ( The Implementation of Convolutional Neural Network Method for Agricultural Plant Classification in High Resolution Imagery ),” Geomatika, vol. 24, no. 2, pp. 61–68, 2018. DOI: https://doi.org/10.24895/JIG.2018.24-2.810
N. Kasim and G. S. Nugraha, “Pengenalan Pola Tulisan Tangan Aksara Arab Menggunakan Metode Convolution Neural Network,” J. Teknol. Informasi, Komputer, dan Apl. (JTIKA ), vol. 3, no. 1, pp. 85–95, 2021, doi: 10.29303/jtika.v3i1.136. DOI: https://doi.org/10.29303/jtika.v3i1.136
Sam’ani and M. H. Qamaruzzaman, “Pengenalan Huruf Dan Angka Tulisan Tangan Mengunakan Metode Convolution Neural Network ( CNN ),” J. Speed – Sentra Penelit. Eng. dan Edukasi, vol. 9, no. 2, pp. 55–64, 2017.
E. S. Udkhiati Mawaddah, Hendrawan Armanto, “Prediksi Karakteristik Personal Menggunakan Analisis Tanda Tangan Dengan Mengggunakan Metode Convolutional Neural Network (CNN),” J. Ilm. Tek. Inform., vol. 15, no. 1, pp. 123–133, 2021.
Copyright (c) 2021 Oddy Virgantara Putra, Aziz Musthafa, Muhammad Nur, Muhamad Rido
This work is licensed under a Creative Commons Attribution 4.0 International License.
