Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Network Engineering Research Operation [NERO] JISKa (Jurnal Informatika Sunan Kalijaga) Sewagati Computer Architecture and Signal Processing
Dwi Sunaryono
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 31 Documents
Search

 1   2   3   4 
Phonics Recognition for Indonesian Dialects Using PNCC and RNN-GRU Nayya Kamila Putri Yulianto; Ratih Nur Esti Anggraini; Dwi Sunaryono
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.533

Abstract

Phonics plays an important role in helping learners develop reading and spelling skills by linking sounds to letters. However, applying phonics in multilingual environments such as Indonesia can be challenging due to the influence of regional dialects, especially for non-native English speakers. In this study, a phonics recognition system is developed using several deep learning approaches. The dataset consists of 986 audio recordings collected from 38 speakers, including both native and non-native English speakers from different regions in Indonesia. To improve data diversity, augmentation techniques such as pitch shifting and speed perturbation are applied. Feature extraction is performed using MFCC and PNCC, followed by classification using CNN, RNN-GRU, and Transformer models. The results show that the RNN-GRU model with PNCC features achieves the best performance, with an accuracy of 94.59% and an F1 Score of 0.946. Compared to previous work using SVM and MFCC, this approach provides better results. It is also observed that PNCC is more robust in handling pronunciation variations, and that dialect differences have a noticeable impact on model performance. Overall, this study highlights the importance of considering dialect variation in phonics recognition and shows how deep learning can be used to build more adaptive speech-based learning systems.
Co-Authors ABDUL MUNIF Adian Latifa Nurrohman Agus Budi Raharjo Agus Budi Raharjo Ahmadiyah, Adhatus Solichah Akbar, Rizky Januar Anang Kunaefi, Anang Andy Rachman Anggraini, Ratih Nur Esti Annisa U Istighotsah Baskoro, Fajar Diana Purwitasari Erlinda Argyanti Nugraha Erlinda Argyanti Nugraha F.X. Arunanto Fajar Baskoro Fajar Baskoro Filardha, Khalifa Fransiscus Xaverius Arunanto Henning Titi Ciptaningtyas I Made Krisna Widhiastra Imam Kuswardayan Imam Mukhlash Isye Arieshanti Johanes Andre Ridoean Kharisma Muchammad Maheswari, Clarissa Luna Maulana, Agha Maulina Nur Istiqomah Misbakhul Munir Irfan Subakti Mohammad Oktri Radity Muhammad Najib Muhammad Shulhan Khairy Nayya Kamila Putri Yulianto Novita Retno Puji Lestari Nugroho Wicaksono Peni Sriwahyu Natasari Rachman, Andy Risky Dwi Setiyawan Riyanarto Sarno Rizky Widhanto Herlambang Romin Adi Santoso Rully Soelaiman Sally Indah Khansa Sally Indah Khansa Sarwosri Sarwosri Setiaputra G, Riswandy Shinta Pramesti Siti Rochimah Stefanus Candra Kusuma Wardana Umi Laili Yuhana Vika Fitratunnany Insanittaqwa Wahyu Suadi Yunita Ardilla Zahrul Zizki Dinanto