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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics JAIS (Journal of Applied Intelligent System)
Eko Hari Rachmawanto
Dian Nuswantoro University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Improved Javanese script recognition using custom model of convolution neural network Ajib Susanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Md Kamruzzaman Sarker
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6629-6636

Abstract

Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.
Co-Authors Adhitya Nugraha Ajib Susanto Akbar Aji Nugroho Christy Atika Sari De Rosal Ignatius Moses Setiadi Edi Jaya Kusuma Egia Rosi Subhiyakto Giovani Ardiansyah Heru Agus Santoso Ibnu Utomo Wahyu Mulyono Kunio Kondo Md Kamruzzaman Sarker Md Kamruzzaman Sarker Musfiqur Rahman Sazal Nanna Suryana Herman Nova Rijati Pulung Nurtantio Andono Ruri Suko Basuki Suprayogi Suprayogi Tan Samuel Permana