Calligraphy Text Types Recognition Using Learning Vector Quantization
DOI:
https://doi.org/10.55123/jomlai.v1i4.1653Keywords:
ANN, Calligraphy, Classification, LVQ, Text RecognitionAbstract
Calligraphy is the art of beautiful writing. The term calligraphy comes from simplified English (calligraphy) taken from the Latin word "kalios" which means beautiful, and "graph" which means writing or script. The art of writing Arabic letters is called the science of khat, known as the science of Arabic calligraphy or Islamic calligraphy. There are many types and varieties of Islamic calligraphy. Each has a different form and function. There are seven types of calligraphy that are popular and known by lovers of calligraphy art in Indonesia, such as, Khat Naskhi, Tsuluts, Farisi, Riq'ah, Diwani, Diwani Jali, and Kufi. The method commonly used to identify what type of calligraphy is made is by looking directly at the shape and characteristics of the calligraphy itself (calligraphy experts). Here the author tries to create a computerized calligraphy type recognition system using the Learning Vector Quantization method. Where this method is a method that works with each unit of output representing a class. So with this system, we can recognize the type of calligraphy text computerized. The accuracy value obtained in the results of calligraphy image recognition is 75%.
References
Rosnawati, A. Syukri, Badarussyamsi, and A. F. Rizki, “Aksiologi Ilmu Pengetahuan dan Manfaatnya bagi Manusia,” Jurnal Filsafat Indonesia, vol. 4, no. 2, pp. 186–194, 2021.
K. M. Simatupang, “Tinjauan Yuridis Perlindungan Hak Cipta dalam Ranah Digital,” Jurnal Ilmiah Kebijakan Hukum, vol. 15, no. 1, pp. 67–80, 2021.
U. H. Salsabila, M. U. Ilmi, S. Aisyah, N. Nurfadila, and R. Saputra, “Peran Teknologi Pendidikan dalam Meningkatkan Kualitas Pendidikan di Era Disrupsi,” Journal on Education, vol. 3, no. 1, pp. 104–112, 2020.
I. G. N. Santika, “Grand Desain Kebijakan Strategis Pemerintah Dalam Bidang Pendidikan Untuk Menghadapi Revolusi Industri 4.0,” Jurnal Education and development, vol. 9, no. 2, pp. 369–377, 2021.
L. Eka Retnaningsih and S. Patilima, “Kurikulum Merdeka pada Pendidikan Anak Usia Dini,” SELING: Jurnal Program Studi PGRA, vol. 8, no. 2, pp. 143–158, 2022.
S. B. Dito and H. Pujiastuti, “Dampak Revolusi Industri 4.0 Pada Sektor Pendidikan: Kajian Literatur Mengenai Digital Learning Pada Pendidikan Dasar dan Menengah,” Jurnal Sains dan Edukasi Sains, vol. 4, no. 2, pp. 59–65, 2021.
A. D. Hamdani, N. Nurhafsah, and S. Silvia, “Inovasi Pendidikan Karakter Dalam Menciptakan Generasi Emas 2045,” JPG: Jurnal Pendidikan Guru, vol. 3, no. 3, pp. 170–178, 2022.
H. Susanti, “Manajemen Pendidikan, Tenaga Kependidikan, Standar Pendidik, dan Mutu Pendidikan,” Asatiza: Jurnal Pendidikan, vol. 2, no. 1, pp. 33–48, 2021.
F. Mulyani and N. Haliza, “Analisis Perkembangan Ilmu Pengetahuan dan Teknologi (Iptek) Dalam Pendidikan,” Jurnal Pendidikan dan Konseling (JPDK), vol. 3, no. 1, pp. 101–109, 2021.
I. N. Husna et al., “Rancang Bangun Sistem Deteksi Dan Perhitungan Jumlah Orang Menggunakan Metode Convolutional Neural Network CNN),” in Seminar Nasional Fortei Regional 7, 2022, pp. 1–6.
Cecep Abdul Cholik, “Perkembangan teknologi Informasi Komunikasi / ICT dalam Berbagai Bidang,” Jurnal Fakultas Teknik, vol. 2, no. 2, pp. 39–46, 2021.
A. A. Fardhani, D. Insani, N. Simanjuntak, and A. Wanto, “Prediksi Harga Eceran Beras Di Pasar Tradisional Di 33 Kota Di Indonesia Menggunakan Algoritma Backpropagation,” Jurnal Infomedia, vol. 3, no. 1, pp. 25–30, 2018.
A. Wanto et al., “Levenberg-Marquardt Algorithm Combined with Bipolar Sigmoid Function to Measure Open Unemployment Rate in Indonesia,” in The 3rd International Conference ofComputer, Environment, Agriculture, Social Science, Health Science, Engineering andTechnology (ICEST), 2021, no. 1, pp. 22–28.
M. A. P. Hutabarat, M. Julham, and A. Wanto, “Penerapan Algoritma Backpropagation Dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota di Sumatera Utara,” Jurnal semanTIK, vol. 4, no. 1, pp. 77–86, 2018.
W. Saputra, J. T. Hardinata, and A. Wanto, “Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed,” JITE (Journal of Informatics and Telecommunication Engineering), vol. 3, no. 1, pp. 163–174, Jul. 2019.
P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
A. Wanto et al., “Analysis of Standard Gradient Descent with GD Momentum And Adaptive LR for SPR Prediction,” 2018, pp. 1–9.
N. L. W. S. R. Ginantra et al., “Performance One-step secant Training Method for Forecasting Cases,” Journal of Physics: Conference Series, vol. 1933, no. 1, pp. 1–8, 2021.
A. Wanto et al., “Epoch Analysis and Accuracy 3 ANN Algorithm using Consumer Price Index Data in Indonesia,” in Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology (ICEST), 2021, no. 1, pp. 35–41.
A. Wanto et al., “Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
T. Afriliansyah et al., “Implementation of Bayesian Regulation Algorithm for Estimation of Production Index Level Micro and Small Industry,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
E. Hartato, D. Sitorus, and A. Wanto, “Analisis Jaringan Saraf Tiruan Untuk Prediksi Luas Panen Biofarmaka di Indonesia,” Jurnal semanTIK, vol. 4, no. 1, pp. 49–56, 2018.
B. K. Sihotang and A. Wanto, “Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang,” Jurnal Teknologi Informasi Techno, vol. 17, no. 4, pp. 333–346, 2018.
J. Wahyuni, Y. W. Paranthy, and A. Wanto, “Analisis Jaringan Saraf Dalam Estimasi Tingkat Pengangguran Terbuka Penduduk Sumatera Utara,” Jurnal Infomedia, vol. 3, no. 1, pp. 18–24, 2018.
I. S. Purba and A. Wanto, “Prediksi Jumlah Nilai Impor Sumatera Utara Menurut Negara Asal Menggunakan Algoritma Backpropagation,” Jurnal Teknologi Informasi Techno, vol. 17, no. 3, pp. 302–311, 2018.
I. S. Purba et al., “Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
R. E. Pranata, S. P. Sinaga, and A. Wanto, “Estimasi Wisatawan Mancanegara Yang Datang ke Sumatera Utara Menggunakan Jaringan Saraf,” Jurnal semanTIK, vol. 4, no. 1, pp. 97–102, 2018.
A. Wanto, “Prediksi Produktivitas Jagung Indonesia Tahun 2019-2020 Sebagai Upaya Antisipasi Impor Menggunakan Jaringan Saraf Tiruan Backpropagation,” SINTECH (Science and Information Technology), vol. 1, no. 1, pp. 53–62, 2019.
W. Saputra, J. T. Hardinata, and A. Wanto, “Resilient method in determining the best architectural model for predicting open unemployment in Indonesia,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–7, 2020.
I. A. R. Simbolon, F. Yatussa’ada, and A. Wanto, “Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia,” Jurnal Informatika Upgris, vol. 4, no. 2, pp. 163–169, 2018.
A. Wanto and J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–8, 2020.
C. Diao, D. Kleyko, J. M. Rabaey, U. C. Berkeley, and B. A. Olshausen, “Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing,” in International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1–9.
Y. Zheng, X. Ye, and T. Wu, “Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition,” Computational Intelligence and Neuroscience, vol. 2021, pp. 1–10, 2021.
S. Nain and P. Chaudhary, “An astute LVQ approach using neural network for the prediction of conditional branches in pipeline processor,” EAI Endorsed Transactions on Scalable Information Systems, vol. 8, no. 31, pp. 1–11, 2021.
O. Okfalisa, E. Budianita, M. Irfan, H. Rusnedy, and S. Saktioto, “The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3,” IT Journal Research and Development, vol. 5, no. 2, pp. 158–170, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Mhd Furqan, Abdul Halim Hasugian, Ziqra Addilah

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:their article is original, written by the mentioned author(s),
- has never been published before,
- does not contain statements that violate the law, and
- does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
- Copyright and other proprietary rights related to the article, such as patents,
- The right to use the substance of the article in its own future works, including lectures and books,
- The right to reproduce the article for its own purposes,
- The right to archive all versions of the article in any repository, and
- The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published



















