Analysis of Gender Identification in Bahasa Indonesia using Supervised Machine Learning Algorithm
Evawaty Tanuar, Edi Abdurachman, Ford Lumban Gaol, Lukas Lukas · 2020
Gender classification or identification is an interesting research area in speech or voice signal processing. It is a promising research area, and there still a room for improvements, especially in the localization context. There are not many researches related gender identification in Bahasa Indonesia. Most of the research found are in English, some are in Chinese, Korea, Arab, France. This paper will used primary data, self-collected in Bahasa Indonesia to identify the gender using the supervised machine learning algorithm. MFCC is used as the feature extraction algorithm for input in the machine learning. After comparing several algorithms: Artificial Neural Network, SVM and K-Nearest Neighbors (KNN) algorithm, ANN shows more promising result then others. There are 2.735 primary data used in this research. The result in this research will be used in future experiment about the impact of gender classification in voice recognition in Bahasa Indonesia.