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All Journal TEKNIK INFORMATIKA Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Pseudocode Teknika: Jurnal Sains dan Teknologi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Hendra Bayu Suseno
Universitas Islam Negeri Syarif Hidayatullah Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Implementation of Convolutional Neural Network with VGG-16 Architecture in Digital Hiragana Handwriting Image Recognition Hendra Bayu Suseno; Fitri Mintarsih; Victor Amrizal; Rheditia Ferdiansyah; Tjut Awaliyah Zuraiyah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.65

Abstract

The number of Japanese language learners in Indonesia ranks second at 711,732 people. Hiragana is the first letter to be learned, especially at the beginner level and is usually learned before Katakana and Kanji. Some characters in Hiragana have similar main forms such as nu (ぬ) and me (め), ne (ね) and wa (わ), thus adding complexity to the recognition process. Like previous research that created a Hiragana pronunciation learning application and previous research that was an English writing learning application, allowing people to learn on their own, by applying CNN (Convolutional Neural Network) to recognize written characters, researchers were inspired to apply this in learning to write Hiragana letters. Therefore, researchers created a digital Hiragana handwriting recognition model using the VGG-16 CNN Architecture method so that the model created can later be used in a Hiragana learning application for writing. This study used a dataset in the form of digital Hiragana handwriting images totaling 1518 data with 33 data for each label (46 types of letters). The hyperparameters used in this study to train the model were 5 epochs, a batch size of 32, the Adam Optimizer, and a Learning rate of 0.001. Based on the test results with the aforementioned parameters, the Accuracy value was 98.55%, Precision was 98.91%, Recall was 98.55%, and the F1-Score was 98.51%.
Co-Authors Addini Yusmar Alfatta Reza Andi Malik Rizki Maulana Andrew Fiade Anif Hanifa Setyaningrum Aryajaya Alamsyah Diana Nur Yastin Feri Fahrianto Fitri Mintarsih Luh Kesuma Wardhani Muhamad Fahri Muhammad Fahri Nurhayati Nurhayati Priyanti Priyanti Rheditia Ferdiansyah Shofi, Imam Marzuki Siti Ummi Masruroh Tjut Awaliyah Zuraiyah Velia Handayani Victor Amrizal Victor Amrizal Viva Arifin Viva Arifin