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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Konvergensi Jurnal Teknik Informatika Insand Comtech : Information Science and Computer Technology Journal Journal of Artificial Intelligence and Digital Business COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Jurnal Pendidikan MIPA
Dewi, Nindian Puspa
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Penerapan Hidden Markov Model (HMM) dan Mel-Frequency Cesptral Coefficients (MFCC) pada E-Learning Bahasa Madura untuk Anak Usia Dini Ubaidi, Ubaidi; Dewi, Nindian Puspa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722477

Abstract

Bahasa Madura is a regional language used in Madura island. This language has many variations of pronunciation and dialect that makes it not easy to learn, even by the local people especially children. There hasn’t been any interesting learning media to learn Bahasa Madura so far. In fact, a fun learning activity is needed to help children to enhance their ability in pronouncing animals’ names, numbers, fruits and things in Bahasa Madura. Thus, it’s considered important to create Bahasa Madura e-learning by implementing the recognition of voice patterns in order to make it easier for the children to learn Bahasa Madura which has several variations of pronunciation only for one single object. This Bahasa Madura e-learning application for young learners is used to introduce Bahasa Madura vocabularies by recognizing the voice pattern recordings which have been processed through MFCC technique as the extracted voice features and HMM as the learning techniques. The implementation of MFCC and HMM as the learning tool to introduce the pronunciation of regional language vocabularies especially Bahasa Madura has never been done before. Therefore, this research is expected to help the young learners to be able to pronounce Bahasa Madura vocabularies properly.  In this study, a number of young learners’ voices were recorded and were set as the trial data. Only the proper voice data that were used—voice data that were considered to be pronounced correctly. The trial method was done through one-single model and multi-model. After doing several simultaneous trials, the result showed the accuracy level. The average accuracy level for one-single model system was 73% (with the highest accuracy reached 75%) and the average accuracy level for multi-model system was 80% (with the highest accuracy reached 81%).
Co-Authors -, Soviana -, Ubaidi ., Ubaidi Arifin, Mohammad Nazir Fauzan Prasetyo Hafsi, Ainur Rofiq Hasan Basri Irwan Darmawan Jannah, Ukhti Raudhatul Listiowarni, Indah Nafisah Nafisah Putra, Willy Arianta Rachmatullah, Sholeh Rahmad Rahmad Ramadhani, Nilam Said, Badar Samsul Arifin Sholihah, Wildanus Ubaidi, Ubaidi