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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) JURNAL REKAYASA TEKNOLOGI INFORMASI Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) JSI (Jurnal sistem Informasi) Universitas Suryadarma METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Sistem Informasi dan Sistem Komputer JNANALOKA JITSI : Jurnal Ilmiah Teknologi Sistem Informasi BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sistem Informasi dan Bisnis Cerdas Smart Techno (Smart Technology, Informatic and Technopreneurship) Adopsi Teknologi dan Sistem Informasi Jurnal Ilmiah Sistem Informasi Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi Kreatif Teknologi dan SIstem Informasi
Setyadi, Hario Jati
Fakultas Ilmu Komputer Dan Teknologi Informasi, Universitas Mulawarman
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Transportation Other

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Journal : knowledge engineering and data science

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Handwriting Character Recognition usingVector Quantization Technique Haviluddin, Haviluddin; Alfred, Rayner; Moham, Ni’mah; Pakpahan, Herman Santoso; Islamiyah, Islamiyah; Setyadi, Hario Jati
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.
Comparative Analysis of BPNN and LVQ for Sundanese Character Recognition Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Nurpadillah, Dinda Izmya; Setyadi, Hario Jati; Taruk, Medi; Alfred, Rayner
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster convergence compared to LVQ, which reached a maximum accuracy of 66.66%. Additionally, BPNN demonstrated better generalization and robustness. At the same time, LVQ was highly sensitive to learning rate variations, leading to unstable accuracy and slower training times. The findings highlight that BPNN is a more effective model for Sundanese script recognition, providing a reliable approach for preserving and digitizing traditional scripts. Future research should explore hybrid models, deep learning approaches, and larger datasets to enhance recognition accuracy and system robustness.
Co-Authors Adiputra, Dimas Bayu ahmad arifin Ahmad Yunus Aji Prasetya Wibawa Al Hidayat, Muhammad Restu Al'Aqsa, Muhammad Ramadhan Alifia, Shafa Nur Aljidannur, Andi Muhammad Rivaldy Amal, Fakhmul Amin Padmo Azam Masa Amin Padmo Azam Masa Andika, Arya Bhima Anggraini, Nela Dwi Anton Prafanto Apriansyah, Muhammad Dandi Aprilia, Trisna Aprilianto, Riky Ardana, Utari Widya Ari Pradhana, Alvin Arif, Afdinal Arinda Mulawardani Kustiawan Arviani, Syilla Asnan Fadjri Wahyudi Aulia, Hadriani Avivah, Nur Ayu Rusnawati Azhari, Ikmal Ali Badaruddin Bin Halib Basani, Yuniarta Budiman, Edy Budiman, Edy Chrisman Bonor Sinaga Dinda Izmya Nurpadillah Dwicky Ari Pandawa Dyna Marisa Khairina Fadhilah, Farah Fahriza, Ridho Fajar Syafatoni Raihanadif Fakhmul Amal Felix Andika Dwiyanto Galih Yudha Saputra Ghalda Melika Gibrani, Muhammad Raza Daffa Gubtha Mahendra Putra Hairunnisa, Namira Aida Handoko, Heldi Hani Purwanti Harianto, Biko Harsyal Kila, Hiskya Hasman, Firnawan Azhari Haviluddin Haviluddin Herman Santoso Pakpahan Husyairi, Rizani Ibrahim , Muhammad Rivani Ibrahim, Muhammad Rivani Inani Inani Indah Fitri Astuti, Indah Fitri Indra Maulana` Indra Maulana Irsyad, Akhmad Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah, Islamiyah Juliani Tangke Jundillah, Muhammad Labib Kamila, Vina Zahrotun Kelvin Wong Kusumawardani, Aditya Putri Listiana Dewi Milasari Madani, Mohammad Ichsan Masa, Amin Padmo Azam Mifthahuddin, Mifthahuddin Mila Kartika Sari Moham, Ni’mah Muhamad Ali Muhammad Arifin Dava Muhammad Arsy Al Fahd Muhammad Bambang Muhammad Faisal Ramdhani Muhammad Fawaz Saputra Muhammad Hisyam Nugroho Muhammad Ibadurrahman Arrasyid Supriyanto Muhammad Labib Jundillah Muhammad Labib Jundillah Muhammad Labib Jundillah Muhammad Luqman Muhammad Rofiif Taqiyyuddin Nabiil Muhammad Shofwan Fikriyannur Muhammad Zulfariansyah Nadia Nadia Nasrullah, Ryanda Putra Nataniel Dengen Nazwa Tri Ananda Nindya Pramudita Ni’mah Moham Ni’mah Moham Nova Nur Fauziah Novianti Puspitasari Nurlaila Nurlaila Nurpadillah, Dinda Izmya Nurul Asmita Nurwahyu, Ferryza Prafanto, Anton Prasetya, Raya Priantono, Ahmad Agung Purnawansyah Purnawansyah Puspitasari, Novianti Putra, Gubtha Mahendra Putut Pamilih Widagdo Putut Pamilih Widagdo Putut Pamilih Widagdo, Putut Pamilih Rabbani , Khalid Mu’afi Rabbani, Zaki Fauzan Rahmad Fitrianto Ramadiani Ramadiani - Rapiq, Rayhan Abdilah Rara Puspa Aisyah Rayhan Fadlur Rahman Rayner Alfred Rayner Alfred Reyfaldho Alfarazel Reza Wardhana Riftika Rizawanti Ririn Yuliani Azahra Zardan Rosita Dewi Rosmasari Rosmasari, Rosmasari Ryan Afriadi Whardana Sagita, Andi Yolanda Sandrina Aulia Saputra, Muhammad Fawaz Saputra, Muhammad Rizq Sari, Upik Kumala Shiva Mutia Maffirotin Sidabutar, Erni Veronica Siti Solikah Yosi Karinda Suharizman Poerwo, Najwa Caesa Putri Ramadhania Supriono Supriono Supriyanto, Muhammad Ibadurrahman Arrasyid Taruk, Medi Tejawati, Andi Tobing, Christina Febriyanti Ulhaq, Dhiya Untu, Zainuddin Untu Upik Kumala Sari Utama, Chorine Jessica Vina Zahrotun Kamila Wage Jason Wahyu Kesuma Bakti Wahyudi, Asnan Fadjri Wanda, Awang Muhammad Trielevy Wardana, Romy Hakim Wardhana, Reza Wati, Masna Widagdo , Putut Pamilih Wong, Kelvin