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All Journal Jurnal Media Infotama Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Riau Journal of Computer Science International Journal of Artificial Intelligence Research JIKO (Jurnal Informatika dan Komputer) INOVTEK Polbeng - Seri Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JOURNAL OF SCIENCE AND SOCIAL RESEARCH MIND (Multimedia Artificial Intelligent Networking Database) Journal JSAI (Journal Scientific and Applied Informatics) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science IJIIS: International Journal of Informatics and Information Systems Journal of Computer System and Informatics (JoSYC) JINAV: Journal of Information and Visualization Journal of Applied Data Sciences JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Journal of Applied Computer Science and Technology (JACOST) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD International Journal for Applied Information Management Journal Corner of Education, Linguistics, and Literature JUSTIN (Jurnal Sistem dan Teknologi Informasi) ProBisnis : Jurnal Manajemen Edu Sociata : Jurnal Pendidikan Sosiologi JOURNAL OF ICT APLICATIONS AND SYSTEM Neraca Manajemen, Akuntansi, dan Ekonomi Cendikia Pendidikan Jurnal Media Akademik (JMA) Bhinneka Multidisiplin Journal Jurnal Manajemen Kewirausahaan dan Teknologi
B. Herawan Hayadi
Universitas Bina Bangsa
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Journal : JSAI (Journal Scientific and Applied Informatics)

 1 
INDENTIFIKASI POLA AKSARA ARAB MELAYU DENGAN JARINGAN SYARAF TIRUAN CONVOLUTIONAL NEURAL NETWORK (CNN) Yanto, Budi; -, Basorudin; -, Jufri; Hayadi, B.Herawan
JSAI (Journal Scientific and Applied Informatics) Vol 3, No 3 (2020): Informatics Science and Implementation
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v3i3.1151

Abstract

Riau province has Malay Arabic script as a traditional cultural heritage of ancient characters that should be preserved; this script is adapted from Arabic writing. This script from Malay Arabic has a unique form that is different from the original Arabic writing adaptation, which is read in a combination of letters forming latin meanings as an introduction to the everyday language of Riau Malay people in the earlier kingdom. Malay Arabic writing became an introduction to the local content of traditional languages in schools. To foster a love for preserving culture, in accordance with current technology that is able to recognize scripting patterns when written in paper, a knowledge base was created by using Matlab software by applying a convolutional Neural Network (CNN) artificial neural network algorithm capable of recognizing script patterns well. The result of image input in the form of handwriting written on paper then in the scanner in the form of JPEG image format. Testing was carried out on four Arabic Malay characters namely alif, ha, la, kho and nun. The result of training for the letter alif (a) epoch is obtained 98 out of 100 iterations with a training length of 3 seconds, furthermore, in validation performance with a result of 0.25013 on epoch 92 of 98 epoch for gradient letters with a value of 0.0071991 on the next epoch 98 in the extras produces an accuracy value of 0.6548 which states the correct result accordingness because it is close to the alif script. In the process of train input the letter kho obtained epoch 80 out of 100 iterations with a training process for 3 seconds, validation performance 0.25153 on epoch 74 out of 80 epoch for check validation with a value of 0.0011682 on the next epoch 80 in the extras obtained an extra value of 0.9326 stated the value is incorrect. Because the result of the extras results in an image that does not come close to the kho letter. Therefore, a study of how the system can recognize Malay Arabic writing patterns with the Convolutional Neural Network (CNN) method because it is very good at identifying image pattern features with an accuracy value of 4.12% of the 10 sample image patterns that have been inputted. With the introduction of imagery patterns from the extraction of features scanned Malay Arabic characters can help the findings of ancient Malay Arabic script as morphological learning of the validity of abstraction of Malay Arabic script is good
INDENTIFIKASI POLA AKSARA ARAB MELAYU DENGAN JARINGAN SYARAF TIRUAN CONVOLUTIONAL NEURAL NETWORK (CNN) Yanto, Budi; -, Basorudin; -, Jufri; Hayadi, B.Herawan
JSAI (Journal Scientific and Applied Informatics) Vol 3 No 3 (2020): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v3i3.1151

Abstract

Riau province has Malay Arabic script as a traditional cultural heritage of ancient characters that should be preserved; this script is adapted from Arabic writing. This script from Malay Arabic has a unique form that is different from the original Arabic writing adaptation, which is read in a combination of letters forming latin meanings as an introduction to the everyday language of Riau Malay people in the earlier kingdom. Malay Arabic writing became an introduction to the local content of traditional languages in schools. To foster a love for preserving culture, in accordance with current technology that is able to recognize scripting patterns when written in paper, a knowledge base was created by using Matlab software by applying a convolutional Neural Network (CNN) artificial neural network algorithm capable of recognizing script patterns well. The result of image input in the form of handwriting written on paper then in the scanner in the form of JPEG image format. Testing was carried out on four Arabic Malay characters namely alif, ha, la, kho and nun. The result of training for the letter alif (a) epoch is obtained 98 out of 100 iterations with a training length of 3 seconds, furthermore, in validation performance with a result of 0.25013 on epoch 92 of 98 epoch for gradient letters with a value of 0.0071991 on the next epoch 98 in the extras produces an accuracy value of 0.6548 which states the correct result accordingness because it is close to the alif script. In the process of train input the letter kho obtained epoch 80 out of 100 iterations with a training process for 3 seconds, validation performance 0.25153 on epoch 74 out of 80 epoch for check validation with a value of 0.0011682 on the next epoch 80 in the extras obtained an extra value of 0.9326 stated the value is incorrect. Because the result of the extras results in an image that does not come close to the kho letter. Therefore, a study of how the system can recognize Malay Arabic writing patterns with the Convolutional Neural Network (CNN) method because it is very good at identifying image pattern features with an accuracy value of 4.12% of the 10 sample image patterns that have been inputted. With the introduction of imagery patterns from the extraction of features scanned Malay Arabic characters can help the findings of ancient Malay Arabic script as morphological learning of the validity of abstraction of Malay Arabic script is good
Co-Authors -, Basorudin Abdi Rahim Damanik Adyanata Lubis Adyanata Lubis Adyanata Lubis, Adyanata agung setiawan Agus Perdana Windarto Agustina Akhmad Zulkifli Alvin, Muhammad Ambarsari, Yuke Aramiko Kayanie Nenden Atryana Arifin, Rita Wahyu Arman Basri Asep Supriyanto Asyahri Hadi Nasyuha Bachtiar, Marsellinus Bayu Kusuma Budi Yanto Budi Yanto Budi Yanto, Budi Budiarto, Mukti Cindy Paramitha Dahliyusmanto, Dahliyusmanto David Setaiwan Dede Nurhasanah Devi Delawati Didik Setiyadi Dwi ASTUTI Dwiastuti, Dwiastuti Edi Roseno Eghar Shafiera Eko Priyanto Engkos Kosasih Enny Widawati Erna Armita, NST Erni Rouza, Erni fatimah Fatimah Franciska, Yuni Furtasan Ali Yusuf Handayani, Meli Hartono Hartono Hayatul Masquroh Henderi . Hendrawati, Tuti Heni Pujiastuti Herlina Latipa Sari Hermawansyah, Hermawansyah Husni Teja Sukmana I Gede Iwan Sudipa Ichsan Firmansyah Ihlas Ahmad Subarkah Ilham Arifin Irawati Irawati irfan, mursyid ISKANDAR JAKA KUSUMA Jaka Kusuma Jaka Tirta Samudra Jaka Tirta Samudra Jin-Mook Kim Jufri -, Jufri Jufri Jufri Juhriah Juhriah, Juhriah Junaesih, R. Karina Andriani Kasman Rukun Kelvin Leonardi Kohsasih Khodijah Hulliyah Kim, Jin-Mook Luth Fimawahib Luth Fimawahib M Haidar Husein Mahdi, Ahmad Masquroh, Hayatul Muadifah, Muadifah muflihah muflihah Muhammad Sadikin Mulyadi, Dadi Musadad Musadad Novendra Adisaputra Sinaga Ovi Sakti Cahyaningtyas P. Eko Prasetyo P.P.P.A.N.W Fikrul Ilmi R.H. Zer Padeli Padeli Pardede, Doughlas Prasiwiningrum, Elyandri Pratama, Gelard Untirtha Puji Sari Ramadhan Rahmulyana, Anjar Raman Raman Raman, Raman Riandini, Meisarah RIKA ROSNELLY Rika Rosnelly Rinanda Rizki Pratama Rinanda Rizki Pratama Rindi Genesa Hatika Rizky Ema Wulansari Rohim, Rouf Rubianto Rudi Gunawan Saepudin Saepudin Safril Safril Sartika Mandasari Sepriyanti, Sepriyanti Siregar, Pariang Sonang Sofiana, Sofa sono, Aji Sudar Suheti, Suheti Suirat, Suirat Sumiyati SUMIYATI SUMIYATI Suwarni Suwarni Swastika, Rulin Tambunan, Fazli Nugraha Teddy Surya Gunawan Toyibah, Toyibah Tutut Herawan Uniba, Muadifah Utomo, Ahmar Dwi Wahdi, Adi Wanayumini Wiwik Handayani Wiwik Novianawati Yuke Ambarsari Yuni Franciska Tarigan Yuningsih, Yuyun Yustiva, Fitriyatul Zakarias Situmorang