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All Journal International Journal of Electrical and Computer Engineering Faktor Exacta STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology International Journal Multidisciplinary Science
Achmad Benny Mutiara
Gunadarma University
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : International Journal Multidisciplinary Science

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CONCEPTUAL REGIONAL ORIGIN RECOGNITION USING CNN CONVOUTION NEURAL NETWORK ON BANDUNG, BOGOR AND CIREBON REGIONAL ACCENTS Adam Huda Nugraha; Achmad Benny Mutiara; Dewi Agushinta Rahayu
International Journal Multidisciplinary Science Vol. 2 No. 2 (2023): June: International Journal Multidiciplinary
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijml.v2i2.696

Abstract

Sound detection is a challenge in machine learning due to the noisy nature of signals, and the small amount of (labeled) data that is usually available. The need for sound detection in Indonesia is quite important because there are many community organizations that form groups according to the land of their origin. Especially in big cities, where people from various tribes gather and exchange cultures. However, it has a disadvantage that affects these tribes, namely the loss of the original culture of certain areas. The Sundanese are the object of this research, including Bandung, Bogor and Cirebon. Voice data is divided into 2 types, namely male and female, each region consists of 50 respondents with 25 male and female voices with a maximum voting time of 1 minute. The method used is CNN architecture based on supervised learning, preprocessing using MFCC (Mel Frequency Cepstral Coefficients) to obtain feature extraction from voice data. CNN architecture is carried out 3 times convolution with max pooling and dropout on each convolution.
Co-Authors Adam Huda Nugraha Adang Suhendra Asep Juarna Dewi Agushinta R. Hustinawaty Hustinawaty, Hustinawaty Millati Izzatillah Rifiana Arief Rina Refianti Rina Refianti Ryan Arya Putra Sayidati Karima Tubagus Maulana Kusuma Zekri Fitra Ramadhan