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All Journal International Journal of Electrical and Computer Engineering Jurnal Masyarakat Informatika Jurnal Simetris Scientific Journal of Informatics Proceeding SENDI_U Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Ilmiah Dinamika Sosial Abdimasku : Jurnal Pengabdian Masyarakat International Journal of Tourism and Hospitality in Asia Pasific Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Mulyono, Ibnu Utomo Wahyu
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19 Susanto, Ajib; Sari, Christy Atika; Rachmawanto, Eko Hari; Mulyono, Ibnu Utomo Wahyu; Mohd Yaacob, Noorayisahbe
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47305

Abstract

Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model.Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%.Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.
Co-Authors Agustina, Feri Aji Kusumah Ramdhani Ajib Susanto Ali, Rabei Raad Christy Atika Sari De Rosal Ignatius Moses Setiadi Dian Indriana Hapsari Doheir, Mohamed Eko Hari Rachmawanto Febrian, Muhamad Rizky Fajar Hadi, M. Sanuri Imang Dapit Pamungkas Indra Kusuma Islam, Hussain Md Mehedul Izza Ulumuddin Ahmad Asshofi Joseph Aldo Irawan Karis Widyatmoko Kusumawati, Yupie Mohd Yaacob, Noorayisahbe N. Nurjanah Ningrum, Novita Kurnia Pramitasari, Ratih Rahayu, Emik Rosyida, Ghaitsa Ardelia Sarker, Md Kamruzzaman Sembiring, Rinawati Sinaga, Daurat SUDARYANTO SUDARYANTO Suprayogi Suprayogi Syafira, Zahra Ghina Zahrotul Umami, Zahrotul