Predicting the Gender of Indonesian Names

TL;DR

This paper presents a char-LSTM approach for predicting Indonesian gender from names, outperforming traditional machine learning methods with over 90% accuracy on full and first names.

Contribution

Introduces a character-level LSTM model for gender prediction in Indonesian names, demonstrating superior accuracy over classical n-gram based methods.

Findings

Char-LSTM achieves 92.25% accuracy with full names.

Using first names alone yields 90.65% accuracy.

The method outperforms traditional machine learning algorithms.

Abstract

We investigated a way to predict the gender of a name using character-level Long-Short Term Memory (char-LSTM). We compared our method with some conventional machine learning methods, namely Naive Bayes, logistic regression, and XGBoost with n-grams as the features. We evaluated the models on a dataset consisting of the names of Indonesian people. It is not common to use a family name as the surname in Indonesian culture, except in some ethnicities. Therefore, we inferred the gender from both full names and first names. The results show that we can achieve 92.25% accuracy from full names, while using first names only yields 90.65% accuracy. These results are better than the ones from applying the classical machine learning algorithms to n-grams.