LSTM-Based Classification of Indonesian Regional Song Lyrics by Language

Abstract

This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language. Unlike prior works that often focus on sentiment analysis or use unbalanced datasets, this research utilizes a balanced dataset consisting of 2,500 lyric segments from five regional languages: Javanese, Sundanese, Batak, Minangkabau, and Banjarese. A comprehensive preprocessing pipeline is applied, including case folding, text cleaning, tokenization, stopword removal, stemming, sequence padding, and label encoding to transform textual data into numerical representations. The model is evaluated using 5-fold cross-validation to ensure robustness and generalization across different data partitions. Experimental results show that the proposed model achieves an accuracy of 95.24%, precision of 95.36%, recall of 95.24%, and F1-score of 95.26%, indicating strong and consistent performance. These findings demonstrate that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages, enabling accurate classification despite similarities in vocabulary and structure. Furthermore, this study contributes to the advancement of natural language processing for low-resource languages and highlights the potential of deep learning approaches in supporting the digital preservation and automatic organization of Indonesian regional cultural content.