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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Informatika dan Teknik Elektro Terapan JURNAL KAJIAN TEKNIK ELEKTRO KACANEGARA Jurnal Pengabdian pada Masyarakat PRoMEDIA IJID (International Journal on Informatics for Development) Jurnal Informatika dan Rekayasa Perangkat Lunak Berdikari : Jurnal Pengabdian kepada Masyarakat KAMI MENGABDI Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK) Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat PANDAWA : Jurnal Pengabdian kepada Masyarakat
Wijonarko, Panji
Universitas 17 Agustus 1945 Jakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Transportation

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Journal : Bulletin of Electrical Engineering and Informatics

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Spoken language identification on 4 Indonesian local languages using deep learning Panji Wijonarko; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4166

Abstract

Language identification is at the forefront of assistance in many applications, including multilingual speech systems, spoken language translation, multilingual speech recognition, and human-machine interaction via voice. The identification of indonesian local languages using spoken language identification technology has enormous potential to advance tourism potential and digital content in Indonesia. The goal of this study is to identify four Indonesian local languages: Javanese, Sundanese, Minangkabau, and Buginese, utilizing deep learning classification techniques such as artificial neural network (ANN), convolutional neural network (CNN), and long-term short memory (LSTM). The selected extraction feature for audio data extraction employs mel-frequency cepstral coefficient (MFCC). The results showed that the LSTM model had the highest accuracy for each speech duration (3 s, 10 s, and 30 s), followed by the CNN and ANN models.
Co-Authors Abdus Salam, Abdus Ahmad Rofii Akbar, Rasyan Almeida, Giovani Costa Amalia Zahra Arestio, Revansyah Arvian James, Bobby Berliana, Niken Bimbang Agus Purnomo Burhanudin Burhanudin Cahya, Ilham Dwi Choirul Mufit Danang Trijayanto Devin, Jonatan Diana Laila Rahmatillah EE Lailatul Putri Gatra, Rahmadhan Hamdala, Marcia Rizky Ismawati, Oktavioni James, Bobby James, Bobby Arvian Jibril, Mohammad Khana, Rajes Khoirunnisa Khoirunnisa Lia Aprilia Lopes, Fatima Odelia M Fajri Hidayat Maharani, Shinta Aulia Marchella, Shelly Mufit, Choirul Muhammad Sobirin Mumtaz, Muhammad Reynaldi Nia Rahmawati, Nia Nova, Rhama Viandra Nurulah, Ilyas Yasin Pratama, Dean Purwati Purwati Putri, EE Lailatul Rabima Rabima Rachman, Muhammad Afif Raffiudin, Muhammad Rajes Khana Ramadhan, Leo Berliandri Ramadhan, Muhamad Lutfi Ramadhan, Muhammad Lutfi Rangki Astiani Rasyan Akbar Ridwan, Jauhan Nuari Rizky Hamdala, Marcia Rohmad Dwi Cahya Rumbiak, Almendo Renal Yustus Sari, Silvia Agustina Wulan Sarifudin, Maulana Setiawan, Aditia Pernando Sri Endah Susilowati Tampubolon, Parlindungan Tasti, Andi Thalita Thaariq, Ath Thoriq, Daffa Raihan Tundo, Tundo Ulfah Fatmala Rizky Wagiman, Wagiman Wawan Iswanto Widagdo, Kristian Widiono Kusumo Wulan Sari, Silvia Agustina yusuf, muhammad maulana Zailanti, Nabila Reva Zain, Zahra Nasywa Zalianti, Nabila Reva