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All Journal JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Engineering and Technology (INAJET) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Informatics and Computer Science (JINACS) Jurnal Informatika Dan Tekonologi Komputer
Anita Qoiriah
Universitas Negeri Surabaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Other

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Penerapan Convolutional Neural Network (CNN) untuk Transliterasi Aksara Bali ke Latin Salwa Aulia Hamidah; Anita Qoiriah
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 2 (2026): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i2.11931

Abstract

Balinese script is a vital cultural heritage that currently faces preservation challenges due to the limited ability of the younger generation to read and understand it manually. This study aims to develop an automatic transliteration system for Balinese script into Latin characters using a Convolutional Neural Network (CNN) based on transfer learning. The dataset consists of 28 classes with 570 images in each class, resulting in a total of 15.960 images, which undergo preprocessing stages including 224×224 resizing, 1/255 normalization, and augmentation, and are split into an 80:20 training and validation ratio. The proposed method compares three pretrained architectures: VGG16 (variations of 20, 50, and 100 epochs), InceptionV3, and ResNet50 (50 epochs) using the Adam optimizer and a 0.0001 learning rate. Performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix. The results show that VGG16 achieves the best performance with a consistent accuracy of 0.97 and the highest precision of 0.98 at 50 epochs, while InceptionV3 reaches 0.83 accuracy and ResNet50 scores 0.55. Based on these results, VGG16 is selected as the best model to be implemented in the transliteration system. In conclusion, the pretrained VGG16 CNN architecture is proven to be highly effective and provides superior accuracy for the automatic transliteration of Balinese script images into Latin characters.
Co-Authors Abastian Dwi Saputra Agus Prihanto Ahmad Haqi Annazili Alfin Falah Sugiarto Andi Iwan Nurhidayat Asmunin Asmunin Atik Wintarti Bramianto Gading Gumilang Dewi Ayu Pratiwi Diah Wulandari Fachriza Dian Adhiatma Gemma Dwi Prasetya Lidya Marcelena Mazidhatul Ilmiyah Muhammad Ainur Rofiq Muhammad Alfian Ma'ruf Muhammad Iqbalul Hidayat Muhammad Jazaal Aufa Naim Rochmawati Natalia Mamahit Nur Lujeng Kinanti Nuzhul Citrasari Dewi Puput Wanarti Rusimamto Rahadian Bisma Respita Yuliana Ramadhani Rina Harimurti Salsabila Maharani Alvananda Herlambang Salwa Aulia Hamidah Septi Kirana Damai Lestari Sindy Caesar Rusnanda Himatul Haliza Syelma Rohmatil Fatikh Talitha irwan Tasya Aulia Maqhfiro Wahyu Muhammad Citra Perdana Wiyli Yustanti Yessy Septiani Yuono Yuni Yamasari